CN109919847B - Method for improving quality of amplified image - Google Patents
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- CN109919847B CN109919847B CN201711331529.1A CN201711331529A CN109919847B CN 109919847 B CN109919847 B CN 109919847B CN 201711331529 A CN201711331529 A CN 201711331529A CN 109919847 B CN109919847 B CN 109919847B
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Abstract
A method for improving quality of an enlarged image for improving edge sharpness when the enlarged image is enlarged from an original image comprising a plurality of original pixels to the enlarged image comprising a plurality of new pixels, the method comprising: carrying out image edge detection on a target pixel area in an original image to judge whether the target pixel area contains an image edge; and when the target pixel area comprises an image edge, performing image sharpness adjustment on a plurality of new pixels, corresponding to the target pixel area and three adjacent pixel areas partially overlapped with the target pixel area, in the amplified image, wherein the target pixel area and the adjacent pixel areas have the same size, and the target pixel area and the adjacent pixel areas respectively comprise one of a plurality of same original pixels.
Description
Technical Field
The embodiments of the present disclosure relate to a method for improving quality of a magnified image, and more particularly, to a method for improving edge sharpness of a magnified image.
Background
In order to enlarge an image with a smaller size to an image with a larger size, the position of a new pixel point can be found according to the position of the pixel point of the original image and the magnification, and the color value of the new pixel point is estimated and filled according to the color values of a plurality of adjacent pixel points of the original image, namely pixel Interpolation (Interpolation). There are many different algorithms for the above pixel interpolation, the most common of which are the nearest neighbor interpolation algorithm and the bilinear interpolation algorithm. The nearest neighbor interpolation algorithm is the simplest and fastest algorithm, but the generated amplified image has obvious distortion phenomena such as mosaic and sawtooth. The bilinear interpolation algorithm increases the calculation quantity of pixel points, obviously improves the visual effect of the image, but has certain phenomena of blurred image edges or blurred fonts. In order to obtain better image amplification quality, many new algorithms are continuously proposed, and most of these new algorithms improve the fidelity of pixel interpolation by expanding the range of reference pixel points and performing a more complicated interpolation calculation mode, but cannot overcome the inherent defect of blurred edges of the amplified image.
Disclosure of Invention
The present disclosure is directed to a method for improving quality of an enlarged image, which overcomes the edge blurring defect of the enlarged image in the conventional image enlarging method by improving sharpness of the edge of the enlarged image. Further, the smoothness of the edge of the enlarged image is improved to improve the phenomenon that the edge of the enlarged image is jagged.
According to the above object of the present disclosure, a method for improving quality of an enlarged image is provided to improve edge sharpness when the enlarged image is enlarged from an original image containing a plurality of original pixels to the enlarged image containing a plurality of new pixels, the method comprising: carrying out image edge detection on a target pixel area in an original image to judge whether the target pixel area contains an image edge; and when the target pixel area comprises an image edge, performing image sharpness adjustment on a plurality of new pixels, corresponding to the target pixel area and three adjacent pixel areas partially overlapped with the target pixel area, in the amplified image, wherein the target pixel area and the adjacent pixel areas have the same size, and the target pixel area and the adjacent pixel areas respectively comprise one of a plurality of same original pixels.
In some embodiments, the image edge detection is performed by comparing the target pixel region with a set of pattern pixel regions to determine whether the target pixel region includes an image edge.
In some embodiments, when the target pixel region does not include an edge of the image, the gray scale value of a new pixel in the enlarged image corresponding to the target pixel region is calculated by pixel interpolation.
In some embodiments, when the target pixel region includes an image edge, the combination of the target pixel region and the adjacent pixel region is used to compare with a set of pattern micro regions to determine a corresponding pattern micro region, so as to obtain a gray scale value of a new pixel corresponding to the target pixel region in the enlarged image according to a gray scale value table corresponding to the corresponding pattern micro region.
In some embodiments, the image sharpness adjustment comprises the following steps: judging whether the target pixel area and the adjacent pixel area have mutual influence; and when the target pixel area and the adjacent pixel area have mutual influence, adjusting the gray-scale value of a new pixel corresponding to the target pixel area and the adjacent pixel area in the enlarged image.
In some embodiments, the method for improving the quality of the magnified image further comprises: when the target micro-region in the enlarged image contains the inclined edge, the image edge smoothness adjustment is carried out on the new pixel contained in the target micro-region so as to improve the edge smoothness of the enlarged image.
In some embodiments, the image edge smoothing adjustment is to maintain the direction of the oblique edge and change the gray scale value of at least one of the new pixels adjacent to the new pixel corresponding to the oblique edge.
In some embodiments, the image edge smoothing adjustment maintains the direction of the oblique edge, and changes the gray level value of at least one of the new pixels adjacent to the new pixel corresponding to the oblique edge, and changes the gray level value of at least one of the new pixels corresponding to the oblique edge.
In some embodiments, the pixel interpolation is a nearest neighbor interpolation algorithm or a bilinear difference algorithm.
In order to make the aforementioned and other features and advantages of the disclosure more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
Aspects of the present disclosure may be better understood from the following detailed description taken in conjunction with the accompanying drawings. It is noted that, in accordance with standard practice in the industry, the various features are not drawn to scale. In fact, the dimensions of the various features may be arbitrarily increased or reduced for clarity of discussion.
Fig. 1 is a schematic distribution diagram of original pixels and new pixels according to a first embodiment of the disclosure.
Fig. 2 is a flowchart illustrating a method of improving magnified image quality according to an embodiment of the present disclosure.
Fig. 3 is a schematic diagram illustrating an original image including a plurality of original pixels according to a first embodiment of the disclosure.
FIG. 4 is a detailed flow chart depicting steps of a method of improving magnified image quality according to an embodiment of the present disclosure.
Fig. 5 is a schematic diagram illustrating a set of pattern pixel regions according to a first embodiment of the disclosure.
Fig. 6 is a schematic diagram illustrating a set of pattern pixel regions according to a second embodiment of the disclosure.
Fig. 7 is a schematic diagram illustrating an original image including a plurality of original pixels according to a first embodiment of the disclosure.
Fig. 8a to 8c are schematic diagrams illustrating image sharpness adjustment according to the first embodiment of the disclosure.
Fig. 9a to 9b are schematic diagrams illustrating an original image including 3 × 3 original pixels and an enlarged image including 5 × 5 new pixels according to a third embodiment of the present disclosure.
Fig. 10a to 10b and fig. 11a to 11b are schematic diagrams illustrating an image edge smoothing adjustment according to a fourth embodiment of the disclosure.
Fig. 10a and 10c and fig. 11a and 11c are schematic diagrams illustrating an image edge smoothing adjustment according to a fifth embodiment of the disclosure.
Fig. 12a to 12b are schematic diagrams illustrating an image edge smoothing adjustment according to a sixth embodiment of the disclosure.
Fig. 12a and 12c are schematic diagrams illustrating an image edge smoothing adjustment according to a seventh embodiment of the disclosure.
Fig. 13a to 13b are schematic diagrams illustrating an image edge smoothing adjustment according to an eighth embodiment of the disclosure.
Fig. 13a and 13c are schematic diagrams illustrating image edge smoothing adjustment according to a ninth embodiment of the disclosure.
Fig. 14a to 14b and fig. 15a to 15b are schematic diagrams illustrating an image edge smoothing adjustment according to a tenth embodiment of the disclosure.
Description of reference numerals:
S1-S4, S11, S12, S21-S24: step (ii) of
100: original image
100 11 -100 44 : original pixel
200 11 -200 22 、300 11 -300 44 、400 11 -400 33 : new pixel
110: target pixel region
120. 130, 140: adjacent pixel region
R: red sub-pixel
G: green sub-pixel
B: blue sub-pixel
Detailed Description
Embodiments of the invention are discussed in detail below. It should be appreciated, however, that the embodiments provide many applicable concepts that can be embodied in a wide variety of specific contexts. The embodiments discussed and disclosed are merely illustrative and are not intended to limit the scope of the invention.
In an embodiment of the present disclosure, an original image including a plurality of original pixels is enlarged into an enlarged image including a plurality of new pixels. For example, assume that an original image including 3 × 3 original pixels is enlarged by 4/3 times to become an enlarged image including 4 × 4 new pixels. One more easily understood concept of image magnification is: placing 4 x 4 imaginary grids on the original image; then, assigning all the grids by a certain method; finally, the grids are expanded to the size of the magnified image. In this way, the enlargement operation of enlarging the original image into an enlarged image is completed.
The relationship of the positions of the original pixels and the new pixels can be understood with the aid of fig. 1. Fig. 1 is a schematic distribution diagram of original pixels and new pixels according to a first embodiment of the disclosure. In FIG. 1, the symbol O represents an original pixel, the symbol + represents a new pixel, and the symbol
Representing overlapping pixels, i.e. an original pixel and a new pixel located at the same position. FIG. 1 is a diagram illustrating an original image including 3 × 3 original pixels enlarged by 4/3 times to become an original image including 4 × 4 new pixelsThe magnified image of (2).
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for improving quality of an enlarged image according to an embodiment of the disclosure. In step S1, an area containing N × M original pixels is taken from the original image, the area is defined as a target pixel area to be processed, and image edge detection is performed on the target pixel area to determine whether the target pixel area contains an image edge, where N, M is a positive integer, and at least one of N and M is a positive integer greater than 2.
Fig. 3 is a schematic diagram of an original image 100 including a plurality of original pixels according to a first embodiment of the disclosure, where fig. 3 is used to exemplarily describe the step S1. The original image 100 includes original pixels 100 11 、100 12 、100 13 、100 14 、100 21 、100 22 、100 23 、100 24 、100 31 、100 32 、100 33 、100 34 、100 41 、100 42 、100 43 、100 44 …, and so on. In the original image 100, a target pixel area 110 including 2 × 2 original pixels is taken, in other words, N = M =2, and the target pixel area 110 includes the original pixels 100 11 、100 12 、100 21 、100 22 。
Referring back to fig. 2, in step S2, the gray-scale values of the new pixels corresponding to the target pixel region in the enlarged image are calculated according to whether the target pixel region includes an image edge. Referring to fig. 4, fig. 4 is a detailed flowchart illustrating steps S1-S2 of a method for improving quality of an enlarged image according to an embodiment of the disclosure. In step S11, the target pixel region is compared with a set of pattern pixel regions. In step S12, whether the target pixel region includes an image edge is determined by comparing the results. The target pixel region and the pattern pixel region have the same size, that is, the pattern pixel region is a group of N × M pixels. Furthermore, the pattern pixel region is a group containing all combinations of image edge patterns.
Fig. 5 is a schematic diagram illustrating a pattern pixel region, and fig. 5 is a schematic diagram illustrating a set of pattern pixel regions according to a first embodiment of the disclosure. As shown in fig. 5, the pattern pixel region is a set of combinations of 16 implementations including 2 × 2 pixels, in other words, N = M =2. In addition, fig. 6 is a schematic diagram illustrating a pattern pixel region according to a second embodiment of the disclosure, and fig. 6 is a schematic diagram illustrating a set of pattern pixel regions according to the second embodiment of the disclosure. As shown in fig. 6, the style pixel region is a set of combinations of 19 implementations including 3 × 3 pixels, in other words, N = M =3.
Referring back to fig. 4, when the target pixel region does not include an image edge, step S21 is performed to calculate a gray level of a new pixel in the enlarged image corresponding to the target pixel region by pixel interpolation (step S24). Specifically, when the target pixel region does not include an image edge, the edge blurring phenomenon is not generated, and thus the gray-scale value of a new pixel corresponding to the target pixel region in the enlarged image can be calculated by using the conventional pixel interpolation method. The existing pixel interpolation method may be, for example, a nearest neighbor interpolation algorithm, a bilinear difference algorithm, or the like. It should be noted that the present disclosure does not limit the calculation method of the pixel interpolation method, and the user can select the suitable current pixel interpolation method according to the actual requirement.
Referring to fig. 4 again, when the target pixel region includes an image edge, step S22 is performed to compare the combination of the target pixel region and three adjacent pixel regions partially overlapping with the target pixel region with a set of pattern micro regions to determine a corresponding pattern micro region. Then, step S23 is performed to obtain the gray level value of the new pixel corresponding to the target pixel region in the enlarged image according to the gray level value table corresponding to the corresponding type micro region.
Fig. 7 is a schematic diagram of an original image 100 including a plurality of original pixels according to a first embodiment of the disclosure. Referring to fig. 3 and fig. 7, the adjacent pixel regions 120, 130, and 140 are the same as the target pixel region 110, in other words, the adjacent pixel regions 120, 130, and 140 respectively include 2 × 2 original pixels. The adjacent pixel region 120 contains the original imageVegetable 100 21 、100 22 、100 31 、100 32 The adjacent pixel region 130 includes the original pixel 100 12 、100 13 、100 22 、100 23 The adjacent pixel region 140 includes the original pixel 100 22 、100 23 、100 32 、100 33 . Specifically, the target pixel region 110 partially overlaps the neighboring pixel regions 120, 130, and 140 and respectively includes one of the same plurality of original pixels (i.e., the original pixel 100) 22 ). It is worth mentioning that the combination of the target pixel region and the three adjacent pixel regions is a region including (2N-1) × (2M-1) pixels.
In the present disclosure, a set of pattern micro-regions is predefined, and a combination of a target pixel region and three adjacent pixel regions is used for comparison, and the comparison result in the pattern micro-regions is matched as the corresponding pattern micro-region. The combination of the target pixel region and the three adjacent pixel regions is the same as the size of the pattern micro region, i.e. the pattern micro region is a group of combinations including (2N-1) × (2M-1) pixels. In addition, a gray scale value table corresponding to the pattern micro-regions is established through a pretest. Therefore, the gray scale value of the new pixel corresponding to the target pixel area in the amplified image can be obtained according to the gray scale value table corresponding to the corresponding type micro area.
Referring back to fig. 2, after step S2, it is determined whether the target pixel region and the adjacent pixel region have mutual influence, and when the target pixel region and the adjacent pixel region have mutual influence, step S3 is performed, i.e., the image sharpness adjustment is performed, and the gray-scale values of the new pixels corresponding to the target pixel region and the adjacent pixel region in the enlarged image are adjusted.
Fig. 8a to 8c are used to exemplarily explain the image sharpness adjustment, and fig. 8a to 8c are schematic diagrams illustrating the image sharpness adjustment according to the first embodiment of the present disclosure. Fig. 8a is an original image including 3 × 3 original pixels, and assuming that the magnification is 4/3, after going through steps S1-S2 of the method for improving the quality of an enlarged image according to the present disclosure, the resultant is fig. 8b, which is an enlarged image including 4 × 4 new pixels. Comparing fig. 8a and 8b, it can be seen that the magnified image (fig. 8 b) shows points (shown in gray) that are not present in the tendency of the oblique edge of the original image (fig. 8 a), which results in the phenomenon of edge blurring when viewing the magnified image (fig. 8 b). Specifically, the target pixel region and the adjacent pixel region in the original image (fig. 8 a) have an influence on each other, and thus an edge blur phenomenon occurs when the image (fig. 8 b) is enlarged. Fig. 8c is the enlarged image after step S3 (i.e., image sharpness adjustment) of the method of improving the quality of the enlarged image according to the embodiment of the present disclosure. Specifically, after the image sharpness adjustment, the enlarged image (fig. 8 b) has a sharp edge display effect because points that do not correspond to the edge trend of the original image (fig. 8 a) are eliminated.
Fig. 9a to 9b are schematic diagrams illustrating an original image including 3 × 3 original pixels and an enlarged image including 5 × 5 new pixels according to a third embodiment of the present disclosure. Fig. 9a is an original image including 3 × 3 original pixels, and assuming that the magnification is 5/3, after steps S1 to S3 of the method for improving the quality of an enlarged image according to the embodiment of the present disclosure, the original image is an enlarged image including 5 × 5 new pixels, which is fig. 9 b. Specifically, after the image sharpness adjustment, the enlarged image (fig. 9 b) has a sharp edge display effect.
It should be noted that, in the embodiment of the present disclosure, the principle of adjusting the image sharpness is that if the original image includes an edge (e.g., a straight line or a curved line) with a certain pixel width, the value obtained by multiplying the line width by the magnification is only an integer if the value is not an integer. For example, if the original image includes a straight line with a pixel width of 1 and the magnification is 1.5 times, the pixel width of the straight line in the magnified image is still 1.
It should be noted that, after a certain target pixel region completes steps S1-S3, steps S1-S3 are continued to be performed on the next target pixel region containing N × M original pixels until all the original pixels in the original image are contained in the combination of the target pixel region and the three adjacent pixel regions partially overlapping with the target pixel region. For example, referring to FIG. 3, after the target pixel region 110 completes the steps S1-S3, the steps are followedThen, the next pixel is composed of 2 × 2 original pixels (i.e. original pixel 100) 13 、100 14 、100 23 、100 24 ) Performs steps S1-S3. For example, referring to fig. 3, after all the original pixels in the first row and the second row of the original image are included in the target pixel area and the combination of three adjacent pixel areas partially overlapping the target pixel area, the next original pixel (i.e. the original pixel 100) is continued to include 2 × 2 original pixels 31 、100 32 、100 41 、100 42 ) Performs steps S1-S3. And so on.
Referring back to fig. 2, after step S3, it is determined whether the target micro region in the enlarged image includes a slant edge, and when the target micro region in the enlarged image includes a slant edge, step S4 is performed to perform image edge smoothing adjustment on new pixels included in the target micro region to improve the edge smoothness of the enlarged image. The image edge smoothing adjustment is to maintain the direction of the inclined edge and change the gray-scale value of at least one of the new pixels adjacent to the new pixel corresponding to the inclined edge. Alternatively, the image edge smoothing adjustment is to maintain the direction of the oblique edge, and change the gray-scale value of at least one of the new pixels adjacent to the new pixel corresponding to the oblique edge, and change the gray-scale value of at least one of the new pixels corresponding to the oblique edge. In other words, the target micro-region may be one or more regions in the enlarged image, and therefore, for the embodiment of the present disclosure, step S4 is sequentially performed for all the target micro-regions.
Fig. 10a to 10c are schematic views illustrating an image edge smoothing adjustment according to a fourth embodiment and a fifth embodiment of the present disclosure, and fig. 10a to 10c are schematic views illustrating the image edge smoothing adjustment according to the fourth embodiment and the fifth embodiment. FIG. 10a presents a target micro-region in a magnified image containing a sloping edge. Such a slanted edge will cause an edge-saw phenomenon when viewed. Fig. 10b is a diagram illustrating an image after edge smoothing adjustment according to a fourth embodiment of the disclosure. FIG. 10b presents the image edge smoothing adjustmentThe direction of the oblique edge of fig. 10a is maintained and two new pixels 200 adjacent to the new pixel corresponding to the oblique edge are adjusted 12 、200 21 The gray scale value of the sub-pixel. For FIG. 10b, the new pixel 200 has been changed 12 Of the red sub-pixel R, wherein the new pixel 200 12 The gray level of the red sub-pixel R is equal to the new pixel 200 11 Gray-scale value of red sub-pixel, new pixel 200 22 Or the average of the two. Furthermore, the new pixel 200 is changed 21 The gray level of the blue sub-pixel B, wherein the new pixel 200 21 The gray level of the blue sub-pixel B is equal to the new pixel 200 22 The gray level of the blue sub-pixel, the new pixel 200 11 Or the average of the two. In addition, fig. 10c is a schematic diagram after the image edge smoothing adjustment according to the fifth embodiment of the present disclosure. FIG. 10c differs from FIG. 10b in that FIG. 10c has changed the new pixel 200 12 The gray-scale value of the green sub-pixel G of (1), wherein the new pixel 200 12 The gray scale value of the green sub-pixel G is equal to the new pixel 200 11 Green sub-pixel, new pixel 200 22 Or the average of the two. Furthermore, FIG. 10c changes the new pixel 200 21 The gray-scale value of the green sub-pixel G of (1), wherein the new pixel 200 21 The green sub-pixel G has a gray scale value equal to the new pixel 200 22 Green sub-pixel, new pixel 200 11 Or the average of the two. Specifically, after the image edge is adjusted smoothly, the phenomenon of original edge sawtooth is smoothed.
Fig. 11a to 11c are schematic views illustrating an image edge smoothing adjustment according to a fourth embodiment and a fifth embodiment of the present disclosure. Fig. 11a to 11c are similar to fig. 10a to 10c, that is, fig. 11b is a schematic diagram after the image edge smoothing adjustment according to the fourth embodiment of the disclosure, and fig. 11c is a schematic diagram after the image edge smoothing adjustment according to the fifth embodiment of the disclosure, with the difference that fig. 11a shows a target micro-region including a tilted edge in the enlarged image, and the target micro-region includes 4 × 4 new pixels. The rest is substantially the same as fig. 10a to 10c, and will not be described again.
Fig. 12a to 12c are schematic diagrams illustrating an image edge smoothing adjustment according to a sixth embodiment and a seventh embodiment of the disclosure. FIG. 12a presents a target micro-region in a magnified image containing a sloping edge. Such a slanted edge will cause an edge jaggy phenomenon when viewed. Fig. 12b is a schematic diagram after image edge smoothing adjustment according to a sixth embodiment of the present disclosure. FIG. 12b presents the image edge smoothing adjustment to maintain the orientation of the slanted edge of FIG. 12a and to adjust two new pixels 300 corresponding to the end points of the slanted edge in FIG. 12a 11 、300 44 And adjusts the new pixel 300 adjacent to the new pixel corresponding to the oblique edge in fig. 12a 12 、300 21 、300 23 、300 32 、300 34 、300 43 The gray-scale value of the sub-pixel. For FIG. 12b, the new pixel 300 is shown 11 The gray level of the red sub-pixel R is copied to the new pixel 300 12 The gray level of the red sub-pixel R, and the new pixel 300 44 The gray level of the blue sub-pixel B is copied to the new pixel 300 43 The gray level of the blue sub-pixel B. Furthermore, the new pixel 300 is changed 21 、300 32 The gray level of the blue sub-pixel B, wherein the new pixel 300 21 The gray level of the blue sub-pixel B is equal to the new pixel 300 22 The gray level value of the blue sub-pixel, the new pixel 300 32 The gray level of the blue sub-pixel B is equal to the new pixel 300 33 The gray scale value of the blue sub-pixel; the new pixel 300 is changed 23 、300 34 The gray-scale value of the red sub-pixel R, wherein the new pixel 300 23 The gray level of the red sub-pixel R is equal to the new pixel 300 22 The gray level value of the red sub-pixel, the new pixel 300 34 The gray level of the red sub-pixel R is equal to the new pixel 300 33 The gray-scale value of the red sub-pixel. Furthermore, FIG. 12c, like FIG. 11b in combination with FIG. 11c, i.e. like FIG. 11c, corresponds to the figure at odd columns12a two sub-pixels (R and G, or G and B) of the new pixel adjacent to the new pixel of the slanted edge are assigned; that is, like fig. 11B, one sub-pixel (B or R) of the adjacent new pixel of the new pixel located at the even column corresponding to the oblique edge in fig. 12a is assigned. Specifically, after the image edge is adjusted smoothly, the phenomenon of original edge sawtooth is smoothed.
Fig. 13a to 13c are used to illustrate the image edge smoothing adjustment, and fig. 13a to 13c are schematic diagrams illustrating the image edge smoothing adjustment according to the eighth embodiment and the ninth embodiment of the present disclosure. Fig. 13a presents the target micro-region in the magnified image containing the oblique edges. Fig. 13b is a schematic diagram after image edge smoothing adjustment according to the eighth embodiment of the present disclosure. FIG. 13b adjusts a new pixel 200 adjacent to the new pixel corresponding to the slanted edge 12 The gray-scale value of the sub-pixel of (1), wherein the new pixel 200 12 The gray level of the red sub-pixel R is equal to the new pixel 200 11 Gray scale value of red sub-pixel, 200 21 Gray scale value of red sub-pixel, 200 22 Or the average of the three. Furthermore, fig. 13c is a schematic diagram after image edge smoothing adjustment according to the ninth embodiment of the present disclosure. FIG. 13c differs from FIG. 13b in that FIG. 13c has changed the new pixel 200 12 The gray scale value of the green sub-pixel G of (1), wherein the new pixel 200 12 The green sub-pixel G has a gray scale value equal to the new pixel 200 11 Green sub-pixel of (2) and a gray scale value of 200 21 Green sub-pixel of (2) a gray scale value of 200 22 Or the average of the gray scale value of the green sub-pixel. Specifically, after the image edge is adjusted smoothly, the phenomenon of original edge sawtooth is smoothed.
Fig. 14a to 14b are used to exemplarily describe the image edge smoothing adjustment, and fig. 14a to 14b are schematic diagrams illustrating the image edge smoothing adjustment according to a tenth embodiment of the disclosure. FIG. 14a presents a target micro-region in a magnified image containing a sloping edge. Fig. 14b is a schematic diagram after image edge smoothing adjustment according to a tenth embodiment of the present disclosure. FIG. 14b adjusts the one adjacent to the new pixel corresponding to the oblique edgeNew pixel 400 22 And adjusting a new pixel 400 corresponding to the oblique edge 21 The gray-scale value of the sub-pixel. For FIG. 14b, the new pixel 400 is 21 The gray level value of the red sub-pixel R is copied to the new pixel 400 22 The gray-scale value of the red sub-pixel R. Specifically, after the image edge is adjusted smoothly, the phenomenon of original edge sawtooth is smoothed.
Fig. 15a to 15b are used to exemplarily describe the image edge smoothing adjustment, and fig. 15a to 15b are schematic diagrams illustrating the image edge smoothing adjustment according to a tenth embodiment of the disclosure. Fig. 15a to 15b are similar to fig. 14a to 14b, and the difference is only that the pixel components in fig. 15a and 14a have different sizes. The rest is substantially the same as fig. 14a to 14b, and will not be described again.
In summary, the present disclosure provides a method for improving quality of an enlarged image. The defect of blurred edges of the amplified image of the traditional image amplification method is overcome by improving the definition of the edges of the amplified image. Further, the smoothness of the edge of the enlarged image is improved to improve the phenomenon that the edge of the enlarged image is jagged.
The foregoing outlines features of several embodiments so that those skilled in the art may better understand the aspects of the present disclosure. Those skilled in the art should appreciate that they may readily use the present disclosure as a basis for designing or modifying other processes and structures for carrying out the same purposes and/or achieving the same advantages of the embodiments introduced herein. It should also be understood by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the present disclosure, and that they may make various changes, substitutions and alterations herein without departing from the spirit and scope of the present disclosure.
Claims (8)
1. A method for improving quality of an enlarged image for improving edge sharpness of an enlarged image from an original image comprising a plurality of original pixels to the enlarged image comprising a plurality of new pixels, the method comprising:
performing image edge detection on a target pixel area in the original image to judge whether the target pixel area contains an image edge; and
when the target pixel area comprises the image edge, performing image definition adjustment on the new pixels of the enlarged image corresponding to the target pixel area and three adjacent pixel areas partially overlapped with the target pixel area, wherein the target pixel area and the adjacent pixel areas have the same size, and the target pixel area and the adjacent pixel areas respectively comprise one of the original pixels which are the same,
wherein the image sharpness adjustment comprises the following steps:
judging whether the target pixel area and the adjacent pixel areas have mutual influence; and
when the target pixel area and the adjacent pixel areas have mutual influence, the gray-scale values of the new pixels corresponding to the target pixel area and the adjacent pixel areas in the enlarged image are adjusted.
2. The method of claim 1, wherein the image edge detection is performed by comparing the target pixel region with a set of pattern pixel regions to determine whether the target pixel region includes the image edge.
3. The method according to claim 2, wherein when the target pixel region does not include the image edge, the gray level values of the new pixels corresponding to the target pixel region in the enlarged image are calculated by a pixel interpolation method.
4. The method of claim 2, wherein when the target pixel region includes the edge of the image, the combination of the target pixel region and the neighboring pixel regions is compared with a set of pattern micro-regions to determine a corresponding pattern micro-region, such that gray-scale values of the new pixels in the enlarged image corresponding to the target pixel region are obtained according to a gray-scale value table corresponding to the corresponding pattern micro-region.
5. The method for improving quality of a magnified image of claim 1 further comprising:
when a target micro-region in the enlarged image includes a slant edge, performing an image edge smoothing adjustment on the new pixels included in the target micro-region to improve the edge smoothness of the enlarged image.
6. The method according to claim 5, wherein the image edge smoothing adjustment maintains the direction of the slanted edge and changes the gray level of at least one of the new pixels adjacent to the new pixels corresponding to the slanted edge.
7. The method according to claim 5, wherein the image edge smoothing adjustment maintains the direction of the slanted edge and changes the gray level of at least one of the new pixels adjacent to the new pixel corresponding to the slanted edge and changes the gray level of at least one of the new pixels corresponding to the slanted edge.
8. The method of claim 3, wherein the pixel interpolation is a nearest neighbor interpolation algorithm or a bilinear difference algorithm.
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| CN110334652B (en) * | 2019-07-05 | 2022-06-07 | 无锡睿勤科技有限公司 | Image processing method, electronic device, and storage medium |
| CN110430376B (en) * | 2019-07-25 | 2021-12-28 | 西南交通大学 | Image processing method and system |
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| TWI665640B (en) | 2019-07-11 |
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