CN114612294A - Image super-resolution processing method and computer equipment - Google Patents
Image super-resolution processing method and computer equipment Download PDFInfo
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Abstract
The invention discloses an image super-resolution processing method and computer equipment. The image super-resolution processing method comprises the following steps: acquiring an image to be processed, and determining an amplified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple; for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on the target pixel value and the super-division multiple of the target pixel point; determining a corresponding super-divided pixel value of a target pixel point based on the target gradient, the super-divided multiple and the image to be processed; and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed. According to the method, the target gradient corresponding to the target pixel point and the super-resolution pixel value corresponding to the target pixel point can be determined only through simple operation, the operation capacity required by the method is far smaller than that required by a deep learning algorithm, the method can be applied to products with low operation capacity, and real-time video super-resolution is realized in products with low operation capacity.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to an image super-resolution processing method and a computer device.
Background
The image super-resolution technology is a process of generating a high-resolution image according to a low-resolution image, the video super-resolution technology is to generate a high-resolution image frame according to each image frame in a video to obtain a high-resolution video, and the real-time video super-resolution technology can render the low-resolution video into the high-resolution video when the video is played by a display device.
At present, the hyper-resolution image corresponding to the low-resolution image can be rapidly obtained through a neural network acceleration chip, and then a real-time video hyper-resolution effect with a good effect is obtained, but the chip is high in cost and can only be used for high-end products, if the computing capability required by the algorithm based on deep learning in the chip is high, the hyper-resolution image can not be rapidly obtained through the algorithm based on deep learning on low-middle-end products, and then the real-time video hyper-resolution task can not be completed.
Therefore, the prior art is yet to be further improved.
Disclosure of Invention
The invention provides an image super-resolution processing method and computer equipment, which are used for realizing the quick generation of a super-resolution image in a low-medium end product and further realizing the real-time video super-resolution in the low-medium end product.
In a first aspect, an embodiment of the present invention provides an image super-resolution processing method, including:
acquiring an image to be processed, and determining an amplified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed;
and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed.
In an implementation manner, the determining, according to the image to be processed and a preset hyper-division multiple, a magnified image corresponding to the image to be processed specifically includes:
determining a plurality of target pixel points according to the image to be processed and the super-division multiple;
for each target pixel point, determining a target pixel value of the target pixel point according to the image to be processed;
and determining an amplified image according to the target pixel values respectively corresponding to the target pixel points.
In an implementation manner, the determining, for each target pixel point, a target pixel value of the target pixel point according to the image to be processed specifically includes:
for each target pixel point, determining a first position and a plurality of second positions corresponding to the target pixel point in the image to be processed, wherein the coordinate of the target pixel point is the product of the coordinate of the first position and the super-division multiple, and the displacement between the coordinate of each second position and the coordinate of the first position is equal;
acquiring a first pixel value corresponding to the first position and second pixel values respectively corresponding to the plurality of second positions;
and determining an average pixel value of the first pixel value and each second pixel value, and taking the average pixel value as the target pixel value.
In an implementation manner, the determining a first location and a plurality of second locations corresponding to the target pixel point in the image to be processed specifically includes:
determining a first position corresponding to the target pixel point in the image to be processed;
determining a number of second positions in the image to be processed according to the first positions and preset expansion values, wherein the displacement between each second position and the first position is equal to the expansion value.
In an implementation manner, for each target pixel point in the enlarged image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple specifically includes:
for each target pixel point in the amplified image, acquiring a plurality of reference pixel values corresponding to the target pixel value;
determining an initial gradient corresponding to the target pixel value according to the target pixel value and the plurality of reference pixel values;
and if the initial gradient is smaller than 0, correcting the initial gradient according to the hyperportioning factor to obtain a target gradient.
In one implementation, the image to be processed is in YUV format, the target pixel values comprise target Y component values, each reference pixel value comprises a reference Y component value, and the initial gradients comprise a row-wise Y component initial gradient and a column-wise Y component initial gradient; the determining an initial gradient corresponding to the target pixel value according to the target pixel value and the plurality of reference pixel values specifically includes:
and determining the initial gradient of the Y component in the row direction and the initial gradient of the Y component in the column direction corresponding to the target pixel value according to the target Y component value and the plurality of reference Y component values.
In one implementation, the target gradient includes a Y-component target gradient in a row direction; if the initial gradient is smaller than 0, correcting the initial gradient according to the super-division multiple to obtain a target gradient, which specifically comprises:
if the initial gradient of the Y component in the row direction is less than 0, determining the gradient direction of the initial gradient of the Y component in the row direction according to a plurality of reference Y component values to obtain an intermediate gradient of the Y component in the row direction;
and normalizing the Y component intermediate gradient in the row direction, and correcting the result after normalization according to the hyper-division multiple to obtain the Y component target gradient in the row direction.
In an implementation manner, the normalizing the Y component intermediate gradient in the row direction, and correcting a result after the normalizing according to the super-division multiple to obtain the Y component target gradient in the row direction specifically includes:
normalizing the Y component intermediate gradient in the row direction to obtain a Y component normalized gradient in the row direction;
determining a correction coefficient according to a preset edge intensity coefficient and the hyper-division multiple;
and calculating the product of the normalized gradient of the Y component in the row direction and the correction coefficient to obtain the target gradient of the Y component in the row direction.
In one implementation, the target gradient includes a column-wise Y-component target gradient; if the initial gradient is smaller than 0, correcting the initial gradient according to the super-division multiple to obtain a target gradient, specifically comprising:
if the initial gradient of the Y component in the column direction is less than 0, determining the gradient direction of the initial gradient of the Y component in the column direction according to the plurality of reference Y component values to obtain the intermediate gradient of the Y component in the column direction;
and normalizing the intermediate gradient of the Y component in the column direction, and correcting the result after the normalization according to the hyper-division multiple to obtain the target gradient of the Y component in the column direction.
In one implementation, the normalizing the Y component intermediate gradient in the column direction and correcting the result after the normalizing according to the super-division multiple to obtain the Y component target gradient in the column direction specifically includes:
normalizing the Y component intermediate gradient in the column direction to obtain a Y component normalized gradient in the column direction;
and obtaining the Y component target gradient in the column direction according to the Y component normalized gradient in the column direction and the correction coefficient.
In an implementation manner, the adjusting the amplified image according to the respective corresponding super-divided pixel values of the target pixel points to obtain a super-divided image corresponding to the image to be processed specifically includes:
and for each target pixel point, replacing the target pixel value corresponding to the target pixel point according to the hyper-resolution value corresponding to the target pixel point so as to obtain a hyper-resolution image corresponding to the image to be processed.
In one implementation, the image to be processed is a two-dimensional animated image.
In a second aspect, an embodiment of the present invention provides an image super-resolution processing apparatus, including:
the magnified image determining unit is used for acquiring an image to be processed and determining a magnified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
a super-divided pixel value determining unit, configured to determine, for each target pixel point in the amplified image, a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-divided multiple, and determine a super-divided pixel value corresponding to the target pixel point based on the target gradient, the super-divided multiple, and the image to be processed;
and the hyper-resolution image generating unit is used for replacing the target pixel value corresponding to the target pixel point with the hyper-resolution pixel value corresponding to the target pixel point so as to obtain the hyper-resolution image corresponding to the image to be processed.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the following steps when executing the computer program:
acquiring an image to be processed, and determining an amplified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed;
and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
acquiring an image to be processed, and determining an amplified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed;
and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed.
Compared with the prior art, the embodiment of the invention has the following advantages:
in the embodiment of the invention, an image to be processed is obtained, and an amplified image corresponding to the image to be processed is determined according to the image to be processed and a preset hyper-division multiple; for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed; and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed. In the invention, the target gradient corresponding to the target pixel point and the super-resolution pixel value corresponding to the target pixel point can be determined only through simple operation, namely, the super-resolution processing is carried out on the image to be processed, the processing process of obtaining the super-resolution image does not involve complex operation, the required operation capacity of the method is far smaller than that of the algorithm of deep learning, and the method can be applied to products with low operation capacity. In the invention, after the target gradient corresponding to each pixel point to be processed is calculated, the target pixel point is obtained according to the super-division multiple, the target pixel point is corrected according to the target gradient to obtain a super-division image, in the process of generating the super-division image, each initial pixel point in the amplified image is traversed, the initial pixel value of each initial pixel point is further determined, the amplified image is obtained, each initial pixel point in the amplified image is traversed again, the super-division pixel value of each target pixel point is further determined, the whole process only needs to be traversed twice, the super-division image can be rapidly generated, and the real-time video super-division can be realized in a product with low computing capability.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a super-resolution processing method for an image according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an image super-resolution processing apparatus according to an embodiment of the present invention;
fig. 3 is an internal structural diagram of an image enhancement computer device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The inventor finds that the image super-resolution technology is a process of generating a high-resolution image according to a low-resolution image, the video super-resolution technology is to generate a high-resolution image frame according to each image frame in a video to obtain a high-resolution video, and the real-time video super-resolution technology can render the low-resolution video into the high-resolution video when a display device plays the video.
At present, a neural network acceleration chip is provided for hardware manufacturers such as high-pass, joint department, samsung, Haisi and the like based on a deep learning algorithm, and the neural network acceleration chip has the advantages that the independent algorithm chip has stronger computing power, and the effect of the deep learning algorithm is generally better than that of the traditional image processing algorithm, but the chip has higher cost and is basically only used for high-end products; although real-time performance of the chip is guaranteed on low-resolution tasks such as human faces and gestures at present, computational bottlenecks exist in super-resolution tasks, and the chip is mainly used for non-real-time tasks such as picture super-resolution. The hyper-resolution image cannot be obtained quickly through the deep learning algorithm, and further the real-time video hyper-resolution task cannot be completed.
In order to solve the above problem, in the embodiment of the present invention, an image to be processed is obtained, and an enlarged image corresponding to the image to be processed is determined according to the image to be processed and a preset hyper-division multiple; for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed; and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed. In the invention, the target gradient corresponding to the target pixel point and the super-resolution pixel value corresponding to the target pixel point can be determined only through simple operation, namely, the super-resolution processing is carried out on the image to be processed, the processing process of obtaining the super-resolution image does not involve complex operation, the required operation capacity of the method is far smaller than that of the algorithm of deep learning, and the method can be applied to products with low operation capacity. In the invention, after the target gradient corresponding to each pixel point to be processed is calculated, the target pixel point is obtained according to the super-division multiple, the target pixel point is corrected according to the target gradient to obtain a super-division image, in the process of generating the super-division image, each initial pixel point in the amplified image is traversed, the initial pixel value of each initial pixel point is further determined, the amplified image is obtained, each initial pixel point in the amplified image is traversed again, the super-division pixel value of each target pixel point is further determined, the whole process only needs to be traversed twice, the super-division image can be rapidly generated, and the real-time video super-division can be realized in a product with low computing capability.
The image super-resolution processing method provided by the embodiment of the invention can be applied to electronic equipment, such as a PC (personal computer), a server, a mobile phone, a television and the like. In addition, the functions realized by the method can be realized by calling application program codes through processing in the electronic equipment, and the program codes can be saved in a computer storage medium.
Referring to fig. 1, fig. 1 shows an image super-resolution processing method in an embodiment of the present invention, and in the embodiment, the image super-resolution processing method may include, for example, the following steps:
s1, acquiring an image to be processed, and determining an enlarged image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple.
In this embodiment of the present invention, the image to be processed is an image frame in a low-resolution video, the low-resolution video may be a video being played by an electronic device, and the image to be processed may be an image frame to be played in the low-resolution video. For example, the frame images in the low-resolution video are sorted according to the playing time of the low-resolution video, when the 100 th image frame is played, the 101 th image frame is obtained, and the 101 th image frame is the image to be processed; the low-resolution video can be a local video in the electronic device, and the image to be processed can be any image frame in the low-resolution video.
In the embodiment of the present invention, the resolution of the image to be processed is smaller than the resolution of the enlarged image, and the resolution of the enlarged image is equal to the resolution of the image to be processed multiplied by the super-divide factor. The magnified image may be determined based on the image to be processed and the hyper-resolution.
Specifically, step S1 includes:
and S11, determining a plurality of target pixel points according to the image to be processed and a preset hyper-division multiple.
In the embodiment of the invention, the target pixel points are pixel points in the amplified image, and the determining of the target pixel points means determining the respective corresponding coordinates of the target pixel points, determining the resolution of the amplified image according to the resolution of the image to be processed and the super-division multiple, and further determining the target pixel points according to the resolution of the amplified image. The image to be processed comprises a plurality of pixel points to be processed, and the number of the pixel points to be processed in the image to be processed is multiplied by the preset super-division multiple to be equal to the number of the target pixel points in the amplified image. The resolution of the image to be processed is H × W, the predetermined hyperfraction factor is 2, and the resolution of the enlarged image is H × W, where H is equal to 2 times H, i.e., H ═ 2H, and likewise, W ═ 2W.
For example, the resolution of the image to be processed is 100 × 100, and the predetermined hyper-division factor is 2, then the resolution of the enlarged image is 200 × 200, and it can be known that the target pixel point in the enlarged image is (x, y), where x has a value range of [1,200], and y has a value range of [1,200 ].
And S12, determining the target pixel value of each target pixel point according to the image to be processed.
In the embodiment of the invention, the image to be processed comprises a plurality of pixel points to be processed, the pixel value of each pixel point to be processed is known, and the target pixel value is determined according to the pixel value of each pixel point to be processed. Specifically, a pixel point to be processed corresponding to a target pixel point is determined, and a pixel value of the target pixel point is determined according to the pixel value of the pixel point to be processed and a pixel point in the neighborhood of the pixel point to be processed.
Step S12 includes:
s121, for each target pixel point, determining a first position and a plurality of second positions corresponding to the target pixel point in the image to be processed, wherein the coordinate of the first position multiplied by the super-division multiple is equal to the coordinate of the target pixel point, and the displacement between the coordinate of each second position and the coordinate of the first position is equal.
In the embodiment of the invention, the abscissa and the ordinate of the target pixel point are obtained, the coordinate of the target pixel point is the product of the coordinate of the first position and the overcutting multiple, and the product of the ordinate of the first position and the overcutting multiple is equal to the ordinate of the target pixel point. For example, the preset hyperfine multiple is recorded as: and s, when the coordinates of the target pixel point are (x, y), the coordinates of the pixel point to be processed are (x/s, y/s).
In the embodiment of the present invention, the displacement between the coordinates of each second position and the coordinates of the first position is equal, that is, the second positions are all around the first position.
Specifically, step S121 includes:
s1211, determining a first position corresponding to the target pixel point in the image to be processed;
s1212, determining the second positions according to the first positions and a preset expansion value, wherein the displacement between each second position and the first position is equal to the expansion value.
In an embodiment of the invention, the number of second locations surrounds the first location, and the locations between each of the number of second locations and the first location are equal to the expansion value. The number of second positions may be 8, i.e. 8 second positions are determined based on the first position and the expansion value, centered on the first position.
In order to reduce the data amount, the number of the second positions is 4, and each of the second positions is in the horizontal direction or the vertical direction of the first position, that is, a plurality of the second positions are respectively at the left side of the horizontal direction, the right side of the horizontal direction, the upper side of the vertical direction and the lower side of the vertical direction of the first position. Because the number of the second positions is only 4, the data size is reduced, and the image super-resolution processing speed is further improved. The expansion value (d) is an empirical parameter set manually, and may be 0.5, or the expansion value may be 1.
For example, if the coordinates of the target pixel point are (x, y), then the coordinates of the pixel point to be processed are (x/s, y/s), and then the coordinates of the neighborhood pixel points are:
and
and S122, acquiring a first pixel value corresponding to the first position and second pixel values respectively corresponding to the plurality of second positions, calculating an average pixel value according to the first pixel value and the second pixel values, and taking the average pixel value as the target pixel value.
In the embodiment of the present invention, if the coordinate of the first position or the second position is an integer, the pixel value of the to-be-processed pixel corresponding to the first position is directly obtained in the to-be-processed image, or the pixel value of the to-be-processed pixel corresponding to the second position is obtained. The abscissa or the ordinate of the first position may not be an integer, and therefore, a to-be-processed pixel point corresponding to the first position cannot be found in the to-be-processed image, and a pixel value corresponding to the first position may be determined according to a neighborhood pixel point of the first position.
For example, when the coordinate of the first position is (2,2.5), a first pixel value of a pixel to be processed with the coordinate of (2,3) and a second pixel value of the pixel to be processed with the coordinate of (2,2) in the image to be processed are obtained, and an average value of the first pixel value and the second pixel value is calculated to obtain a pixel value corresponding to the first position with the coordinate of (2, 2.5).
In the embodiment of the invention, the average value of each candidate pixel value and the pixel value to be processed is calculated to obtain the target pixel value. For example, the coordinates of the target pixel point P0 ═ 8, the preset super-division multiple is 4, the preset dilation value is 0.5, and the coordinates of the to-be-processed pixel point P0 ═ 2, and the coordinates of the several neighborhood pixels are: p1 ═ 1.5,2 ═ P2 ═ 2.5,2 ═ P3 ═ 2,2.5 and P4 ═ 2,1.5, pixel values of P0, P1, P2, P3 and P4 are obtained respectively, and the pixel value of the target pixel point P0 is obtained from the pixel values of P0, P1, P2, P3 and P4.
The process of step S12 is implemented according to formula (1).
Wherein Sr is the reciprocal of s, namely Sr is 1/s,
to enlarge the target pixel value of the target pixel point of coordinate (i, j) in the image,
the pixel value to be processed of the pixel point to be processed with the coordinate of (i multiplied by Sr, j multiplied by Sr) in the image to be processed,
is the pixel value to be processed of the pixel point to be processed with the coordinate of (i multiplied by Sr-d, j multiplied by Sr) in the image to be processed,
the to-be-processed pixel value of the to-be-processed pixel point with the coordinate of (i multiplied by Sr + d, j multiplied by Sr) in the to-be-processed image
Is the pixel value to be processed of the pixel point to be processed with the coordinate of (i multiplied by Sr, j multiplied by Sr-d) in the image to be processed,
the pixel value to be processed of the pixel point to be processed with the coordinate of (i multiplied by Sr, j multiplied by Sr + d) in the image to be processed is obtained.
The derivation process of equation (1) is described next.
Determining an initial image corresponding to the image to be processed through interpolation operation, wherein the initial image is obtained by directly amplifying the amplified image by a super-division factor; and after the initial image is obtained, filtering the initial image to obtain the amplified image.
The method comprises the following steps that a plurality of initial pixel points are included, and the number of the initial pixel points is equal to the number of all to-be-processed pixel points in the to-be-processed image multiplied by the super-division multiple; the pixel value of each initial pixel point of the initial image can be determined according to the pixel value of the pixel point to be processed in the image to be processed. Please see formula (2).
Wherein the content of the first and second substances,
the pixel value of the initial pixel point with coordinates (i, j) in the initial image is x i × Sr, y j × Sr, Sr is the reciprocal of s, i.e., Sr 1/s.
In the embodiment of the invention, after the initial image is obtained, the initial image is filtered to obtain the amplified image. In the prior art, an amplified image can be obtained by filtering an initial image through a convolution layer with a convolution kernel size of 3 × 3, specifically, a pixel value of a target pixel point with a coordinate (x, y) in the amplified image is determined through 9 initial pixel points in the initial image, and the coordinates of the 9 initial pixel points are respectively: (x-1, y-1), (x +1, y-1), (x-1, y), (x +1, y), (x-1, y +1), (x +1, y + 1); in order to reduce the calculation amount, only 5 initial pixel points are adopted to determine the pixel value of a target pixel point with the coordinate (x, y) in the amplified image, namely, pixel points with the coordinates (x, y-1), (x-1, y), (x +1, y) and (x, y +1) are taken.
Further, the offset is calculated by the expansion value d and the superscale, see equation (3).
D＝d×s (3)
Wherein D is the offset, D is the expansion value, and s is the over-fraction.
And (4) calculating the pixel value of each target pixel point in the amplified image according to the offset and the initial image, specifically see formula (4).
Wherein the content of the first and second substances,
to enlarge the target pixel value of the target pixel point of coordinate (i, j) in the image,
is the pixel value of the initial pixel point with coordinates (i, j) in the initial image, and D is the offset.
According to the formula (4), when the super-division multiple is 2 and the expansion value D is 0.5, the offset D is 1, the target pixel values of the target pixel points are determined through 5 initial pixel points in the image block with the size of 3 × 3, and the receptive field corresponding to each target pixel point is 3; when the super-division multiple is 4 and the expansion value D is 0.5, the offset D is 2, 5 initial pixel points include 1 central pixel point with coordinates (i, j) and 4 neighborhood pixel points around the central pixel point, the displacement between each neighborhood pixel point and the central pixel point is 2, that is, the target pixel value of the target pixel point is determined by 5 pixel points in the image block with the size of 5 × 5, and the receptive field corresponding to each target pixel point is 5. Therefore, the larger the super-division multiple is, the larger the receptive field corresponding to the target pixel point is. The larger the receptive field corresponding to the target pixel point is, the larger the area expressing the influence on the target pixel point is, and the more image information included by the target pixel point is, so that the receptive field is increased, and the accuracy of subsequent gradient calculation can be improved.
In the embodiment of the present invention, the following formula (4)
Instead of the Xlq expression in equation (2), equation (2) can be obtained. That is, the formula (2) includes: using only 5 initial pixel points to determine the positionThe pixel of the target pixel point with coordinates (x, y) in the large image improves the calculation speed, the amplified image can be obtained more quickly, the offset is increased in the initial image, the receptive field corresponding to the target pixel point is further increased, and the quality of the amplified image is improved.
S2, for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on the target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed.
In the embodiment of the invention, in order to further enhance the edge quality in the magnified image, the target gradient corresponding to each target pixel point is calculated, and the target gradient corresponding to each target pixel point is determined according to the target gradient. The image to be processed is a two-dimensional animation image, and as the edge in the two-dimensional animation image is generally a black line, the edge of the image only needs to be kept unchanged, and the rest part is contracted to smooth the edge.
Specifically, step S2 includes:
s21, acquiring a plurality of reference pixel values corresponding to the target pixel values for each target pixel point in the amplified image;
and S22, determining an initial gradient corresponding to the target pixel value according to the target pixel value and the plurality of reference pixel values.
In the embodiment of the invention, the target pixel value is the pixel value of the target pixel point, and a plurality of reference pixel points corresponding to the target pixel point are determined in the amplified image, wherein the plurality of reference pixel points are all neighborhood pixel points of the target pixel point. That is to say, the amplified image includes a plurality of target pixel points, and for a target pixel point, a neighborhood pixel point of the target pixel point is used as a reference pixel point of the target pixel point (the nature of the reference pixel point is also the target pixel point in the amplified image). And then obtaining reference pixel values respectively corresponding to the plurality of reference pixel points.
In the embodiment of the present invention, the number of the reference pixel points corresponding to the target pixel point is 4, and the reference pixel points are respectively located on the left side of the row direction, the right side of the row direction, the upper side of the column direction, and the lower side of the column direction of the target pixel point. And the displacement between each reference pixel point and the target pixel point is equal to the order of the initial gradient. The initial gradient is a second-order gradient, that is, the displacement between each reference pixel point and the target pixel point is equal to 2. For example, when the coordinates of the target pixel point are (i, j), the coordinates of the reference pixel points are (i-2, j), (i +2, j), (i, j-2), and (i, j +2), respectively.
In the embodiment of the invention, the image to be processed is in YUV format, and "Y" represents brightness, namely a gray value; and "U" and "V" denote chromaticity, which is used to describe the color and saturation of the image. In turn, the object pixel values comprise object Y component values, each reference pixel value comprising a reference Y component value. The initial gradient includes a Y-component initial gradient in a row direction and a Y-component initial gradient in a column direction.
And acquiring an object Y component value corresponding to the object pixel value and a reference Y component value corresponding to each reference pixel value grid respectively. And determining the initial gradient of the Y component in the row direction and the initial gradient of the Y component in the column direction corresponding to the target pixel value according to the target Y component value and the plurality of reference Y component values.
The Y component initial gradient in the row direction and the Y component initial gradient in the column direction corresponding to the target pixel value can be calculated according to the formulas (5) and (6).
Wherein the content of the first and second substances,
is the initial gradient of the Y component in the row direction with coordinates (i, j),
is the reference Y component with coordinates (i, j-2),
is the reference Y component with coordinates (i, j + 2);
is the object Y component value with coordinates (i, j);
is the initial gradient of the Y component in the column direction with coordinates (i, j),
is the reference Y component with coordinates (i-2, j) corresponding thereto,
is the reference Y component with coordinates (i +2, j).
And S23, if the initial gradient is smaller than 0, correcting the initial gradient according to the super-division multiple to obtain a target gradient.
In the embodiment of the invention, the image to be processed is a two-dimensional animation image, and as the edge in the two-dimensional animation image is generally a black line, the edge of the image only needs to be kept unchanged, and the rest part is contracted to smooth the edge, that is, a target pixel point with the initial gradient smaller than 0 is screened out. For a target pixel point, if the initial gradient corresponding to the target pixel point is greater than 0, modifying the initial gradient corresponding to the target pixel point to be 0; and if the initial gradient corresponding to the target pixel point is smaller than 0, correcting the initial gradient corresponding to the target pixel point to obtain a target gradient.
Specifically, step S23 includes:
s231, if the initial gradient of the Y component in the row direction is smaller than 0, determining the gradient direction of the initial gradient of the Y component in the row direction according to a plurality of reference Y component values to obtain a middle gradient of the Y component in the row direction; and normalizing the Y component intermediate gradient in the row direction, and correcting the result after normalization according to the hyper-division multiple to obtain the Y component target gradient in the row direction.
S232, if the initial gradient of the Y component in the column direction is smaller than 0, determining the gradient direction of the initial gradient of the Y component in the column direction according to the plurality of reference Y component values to obtain the intermediate gradient of the Y component in the column direction; and normalizing the intermediate gradient of the Y component in the column direction, and correcting the result after the normalization according to the hyper-division multiple to obtain the target gradient of the Y component in the column direction.
In the embodiment of the present invention, the sign of the initial gradient does not represent the gradient change direction, and the pair is required
And
the direction of (2) is corrected.
The intermediate gradient includes a Y-component intermediate gradient in a row direction and a Y-component intermediate gradient in a column direction. Specifically, when the initial gradient of the Y component in the row direction is less than 0, the intermediate gradient of the Y component may be calculated according to formula (7); when the initial gradient of the Y component in the column direction is less than 0, the Y component intermediate gradient may be calculated according to equation (8).
Wherein the content of the first and second substances,
the intermediate gradient of the Y component in the row direction of the target pixel point with the coordinates of (i, j),
is the intermediate gradient of the Y component in the column direction of the target pixel point with coordinate (i, j). sign () is a sign function, and takes a value of 1 when () is greater than 0, and takes a value of-1 when () is less than 0.
In the embodiment of the invention, the intermediate gradient of the Y component in the row direction is subjected to normalization processing to obtain a normalized gradient of the Y component in the row direction; determining a correction coefficient according to a preset edge intensity coefficient and the hyper-division multiple; and calculating the product of the Y component normalized gradient in the row direction and the correction coefficient to obtain the Y component target gradient in the row direction.
The Y component intermediate gradient in the row direction is normalized by formula (9) and formula (10).
The preset edge strength coefficient is considered as a set hyper-parameter, and S is strengthh S, wherein strengthh is the edge strength coefficient, S is a hyper-division multiple, and S is a correction coefficient. And calculating the product between the correction coefficient and the normalized gradient of the Y component in the row direction to obtain the target gradient of the Y component in the row direction.
In the embodiment of the invention, the intermediate gradient of the Y component in the column direction is subjected to normalization processing to obtain a normalized gradient of the Y component in the column direction; and calculating the product of the normalized gradient of the Y component in the column direction and the correction coefficient to obtain the target gradient of the Y component in the column direction.
The Y component intermediate gradient in the column direction is normalized by formula (9) and formula (11).
Wherein the content of the first and second substances,
the Y component normalized gradient in the column direction is calculated, and the product of the correction coefficient S and the Y component normalized gradient in the column direction is calculated, so that the Y component target gradient in the column direction can be obtained.
In the embodiment of the invention, the edge compensation is realized according to the target gradient and the hyper-division multiple, and the hyper-division pixel value corresponding to the target pixel point can be obtained through a formula (12).
Determining coordinates in the image to be processed as
The pixel value of (i) is set as the corresponding super-divided pixel value of (i, j).
And S3, adjusting the amplified image according to the super-divided pixel values respectively corresponding to the target pixel points to obtain a super-divided image corresponding to the image to be processed.
In the embodiment of the invention, for each target pixel point, the target pixel value corresponding to the target pixel point is replaced according to the hyper-resolution value corresponding to the target pixel point, so as to obtain the hyper-resolution image corresponding to the image to be processed.
In the embodiment of the present invention, the target pixel points are pixel points in the enlarged image, and for each target pixel point, the super-divided pixel value corresponding to the target pixel point is determined through step S3, and the target pixel value corresponding to the target pixel point is replaced with the super-divided pixel value corresponding to the target pixel point, so as to obtain a super-divided image.
For example, for the target pixel point f1, the target pixel point value of f1 is G1, the corresponding super-divided pixel value of f1 is G1, and the pixel value of f1 is set to G1. The above operation is performed on each target pixel point in the enlarged image, that is, the target pixel point of the enlarged image includes: f1, f2, f3, … … fn, and for each target pixel point in f1, f2, f3, … … fn, setting the pixel value of the target pixel point as the hyper-divided pixel value corresponding to the target pixel point, so as to obtain a hyper-divided image.
In the embodiment of the invention, an image to be processed is obtained, and an amplified image corresponding to the image to be processed is determined according to the image to be processed and a preset hyper-division multiple; for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed; and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed. In the invention, the target gradient corresponding to the target pixel point and the super-resolution pixel value corresponding to the target pixel point can be determined only through simple operation, namely, the super-resolution processing is carried out on the image to be processed, the processing process of obtaining the super-resolution image does not involve complex operation, the required operation capacity of the method is far smaller than that of the algorithm of deep learning, and the method can be applied to products with low operation capacity. In the invention, after the target gradient corresponding to each pixel point to be processed is calculated, the target pixel point is obtained according to the super-division multiple, the target pixel point is corrected according to the target gradient to obtain a super-division image, in the process of generating the super-division image, each initial pixel point in the amplified image is traversed, the initial pixel value of each initial pixel point is further determined, the amplified image is obtained, each initial pixel point in the amplified image is traversed again, the super-division pixel value of each target pixel point is further determined, the whole process only needs to be traversed twice, the super-division image can be rapidly generated, and then the real-time video super-division in a product with low computing capability can be realized.
Based on the image super-resolution processing method, an embodiment of the present invention further provides an image super-resolution processing apparatus, referring to fig. 2, including:
the magnified image determining unit is used for acquiring an image to be processed and determining a magnified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
a super-divided pixel value determining unit, configured to determine, for each target pixel point in the amplified image, a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-divided multiple, and determine a super-divided pixel value corresponding to the target pixel point based on the target gradient, the super-divided multiple, and the image to be processed;
and the hyper-resolution image generating unit is used for replacing the target pixel value corresponding to the target pixel point with the hyper-resolution pixel value corresponding to the target pixel point so as to obtain the hyper-resolution image corresponding to the image to be processed.
Based on the image super-resolution processing method, the embodiment of the invention also provides a computer device, which can be a terminal, and the internal structure of the computer device is shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an image super-resolution processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that fig. 3 is only a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The embodiment of the invention provides computer equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the following steps:
acquiring an image to be processed, and determining an amplified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed;
and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
acquiring an image to be processed, and determining an amplified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed;
and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (15)
1. An image super-resolution processing method is characterized by comprising the following steps:
acquiring an image to be processed, and determining an amplified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
for each target pixel point in the amplified image, determining a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-division multiple, and determining a super-division pixel value corresponding to the target pixel point based on the target gradient, the super-division multiple and the image to be processed;
and adjusting the amplified image according to the corresponding super-divided pixel values of the target pixel points respectively to obtain a super-divided image corresponding to the image to be processed.
2. The image super-resolution processing method according to claim 1, wherein the determining, according to the image to be processed and a preset hyper-resolution, a magnified image corresponding to the image to be processed specifically includes:
determining a plurality of target pixel points according to the image to be processed and the super-division multiple;
for each target pixel point, determining a target pixel value of the target pixel point according to the image to be processed;
and determining an amplified image according to the target pixel values respectively corresponding to the target pixel points.
3. The image super-resolution processing method according to claim 2, wherein for each target pixel point, determining a target pixel value of the target pixel point according to the image to be processed specifically comprises:
for each target pixel point, determining a first position and a plurality of second positions corresponding to the target pixel point in the image to be processed, wherein the coordinate of the target pixel point is the product of the coordinate of the first position and the super-division multiple, and the displacement between the coordinate of each second position and the coordinate of the first position is equal;
acquiring a first pixel value corresponding to the first position and second pixel values respectively corresponding to the plurality of second positions;
and determining an average pixel value of the first pixel value and each second pixel value, and taking the average pixel value as the target pixel value.
4. The image super-resolution processing method according to claim 3, wherein the determining a first position and a plurality of second positions corresponding to the target pixel point in the image to be processed specifically includes:
determining a first position corresponding to the target pixel point in the image to be processed;
determining a number of second positions in the image to be processed according to the first positions and preset expansion values, wherein the displacement between each second position and the first position is equal to the expansion value.
5. The image super-resolution processing method according to claim 1, wherein the determining, for each target pixel point in the enlarged image, a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-resolution factor specifically includes:
for each target pixel point in the amplified image, acquiring a plurality of reference pixel values corresponding to the target pixel value;
determining an initial gradient corresponding to the target pixel value according to the target pixel value and the plurality of reference pixel values;
and if the initial gradient is smaller than 0, correcting the initial gradient according to the hyperportioning factor to obtain a target gradient.
6. The image super-resolution processing method according to claim 5, wherein the image to be processed is in YUV format, the target pixel values include target Y-component values, each reference pixel value includes a reference Y-component value, and the initial gradient includes a Y-component initial gradient in a row direction and a Y-component initial gradient in a column direction; the determining an initial gradient corresponding to the target pixel value according to the target pixel value and the plurality of reference pixel values specifically includes:
and determining the initial gradient of the Y component in the row direction and the initial gradient of the Y component in the column direction corresponding to the target pixel value according to the target Y component value and the plurality of reference Y component values.
7. The image super-resolution processing method according to claim 6, wherein the target gradient includes a Y component target gradient in a row direction; if the initial gradient is smaller than 0, correcting the initial gradient according to the super-division multiple to obtain a target gradient, specifically comprising:
if the initial gradient of the Y component in the row direction is less than 0, determining the gradient direction of the initial gradient of the Y component in the row direction according to a plurality of reference Y component values to obtain an intermediate gradient of the Y component in the row direction;
and normalizing the intermediate gradient of the Y component in the row direction, and correcting the result after normalization according to the super-division multiple to obtain the target gradient of the Y component in the row direction.
8. The image super-resolution processing method according to claim 7, wherein the normalizing the Y component intermediate gradient in the row direction and correcting the result of the normalization according to the super-division multiple to obtain a Y component target gradient in the row direction specifically includes:
normalizing the Y component intermediate gradient in the row direction to obtain a Y component normalized gradient in the row direction;
determining a correction coefficient according to a preset edge intensity coefficient and the hyper-division multiple;
and calculating the product of the normalized gradient of the Y component in the row direction and the correction coefficient to obtain the target gradient of the Y component in the row direction.
9. The image super-resolution processing method according to claim 6, wherein the target gradient includes a column-wise Y-component target gradient; if the initial gradient is smaller than 0, correcting the initial gradient according to the super-division multiple to obtain a target gradient, specifically comprising:
if the initial gradient of the Y component in the column direction is less than 0, determining the gradient direction of the initial gradient of the Y component in the column direction according to the plurality of reference Y component values to obtain the intermediate gradient of the Y component in the column direction;
and normalizing the intermediate gradient of the Y component in the column direction, and correcting the result after the normalization according to the hyper-division multiple to obtain the target gradient of the Y component in the column direction.
10. The image super-resolution processing method according to claim 9, wherein the normalizing the Y component intermediate gradient in the column direction and correcting the result of the normalization according to the super-resolution multiple to obtain a Y component target gradient in the column direction specifically includes:
normalizing the Y component intermediate gradient in the column direction to obtain a Y component normalized gradient in the column direction;
and obtaining a Y component target gradient in the column direction according to the Y component normalized gradient in the column direction and the correction coefficient.
11. The image super-resolution processing method according to claim 1, wherein the adjusting the enlarged image according to the respective corresponding super-divided pixel values of the target pixel points to obtain the super-divided image corresponding to the image to be processed specifically comprises:
and for each target pixel point, replacing the target pixel value corresponding to the target pixel point according to the hyper-resolution value corresponding to the target pixel point so as to obtain a hyper-resolution image corresponding to the image to be processed.
12. The image super-resolution processing method according to any one of claims 1 to 11, wherein the image to be processed is a two-dimensional animation image.
13. An image super-resolution processing apparatus, comprising:
the magnified image determining unit is used for acquiring an image to be processed and determining a magnified image corresponding to the image to be processed according to the image to be processed and a preset hyper-division multiple;
a super-divided pixel value determining unit, configured to determine, for each target pixel point in the amplified image, a target gradient corresponding to the target pixel point based on a target pixel value of the target pixel point and the super-divided multiple, and determine a super-divided pixel value corresponding to the target pixel point based on the target gradient, the super-divided multiple, and the image to be processed;
and the hyper-resolution image generating unit is used for replacing the target pixel value corresponding to the target pixel point with the hyper-resolution pixel value corresponding to the target pixel point so as to obtain the hyper-resolution image corresponding to the image to be processed.
14. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method according to any of claims 1 to 12.
15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 12.
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| CN107909545A (en) * | 2017-11-17 | 2018-04-13 | 南京理工大学 | A kind of method for lifting single-frame images resolution ratio |
| CN107967669A (en) * | 2017-11-24 | 2018-04-27 | 腾讯科技（深圳）有限公司 | Method, apparatus, computer equipment and the storage medium of picture processing |
| US20190370938A1 (en) * | 2018-02-11 | 2019-12-05 | Shenzhen Skyworth-Rgb Electronic Co., Ltd. | Image super-resolution method, image super-resolution device, and computer readable storage medium |
| CN111402143A (en) * | 2020-06-03 | 2020-07-10 | 腾讯科技（深圳）有限公司 | Image processing method, device, equipment and computer readable storage medium |
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| CN117036172A (en) * | 2023-08-15 | 2023-11-10 | 禹创半导体(深圳)有限公司 | Image resolution adjustment method, device, equipment and medium |
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