CN116883270A - Soft mirror clear imaging system for lithotripsy operation - Google Patents
Soft mirror clear imaging system for lithotripsy operation Download PDFInfo
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Abstract
The application relates to the technical field of image processing, in particular to a soft-lens clear imaging system for lithotripsy, which is used for determining each edge pixel point and each non-edge pixel point in a target area image by acquiring the target area image corresponding to an imaging image to be cleaned and performing edge detection on the target area image; gray level equalization processing is carried out on each non-edge pixel point in the target area image, so that a first target area enhanced image is obtained; gray value adjustment processing is carried out on edge pixel points and reference pixel points in the enhanced image of the first target area, so that an enhanced image of the second target area is obtained; and determining a sharpened imaging image according to the second target area enhanced image and the imaging image. According to the application, the gray level equalization processing of the non-edge region and the contrast enhancement processing of the edge region are carried out on the target region image, so that the image definition processing effect is effectively improved.
Description
Technical Field
The application relates to the technical field of image processing, in particular to a soft mirror definition imaging system for lithotripsy.
Background
In the process of treating urinary tract calculus by using a soft-lens lithotripsy technology, a soft lens which is soft and does not damage tissues is generally used for detecting the parts such as urethra, bladder, ureter and the like, and a soft-lens image of the part where the calculus is located is acquired. Because the internal environment of the human body organ is complex, the detected soft mirror image is often blurred, and in order to meet the requirement of high precision of the operation, the detected soft mirror image is usually subjected to a sharpening process.
In the conventional image sharpening process, histogram equalization is widely used as the most common image enhancement means because of its simple principle and small calculation amount. However, the histogram equalization can only perform global enhancement or regional enhancement, but cannot perform selected regional enhancement, and the global gray value is stretched during enhancement, and after the soft mirror image obtained by the soft mirror lithotripsy technology is subjected to the sharpening treatment by adopting the histogram equalization, the image is still blurred, and a good enhancement effect cannot be achieved.
Disclosure of Invention
The application aims to provide a soft mirror definition imaging system for lithotripsy, which is used for solving the problem that the conventional soft mirror image definition processing effect is poor.
In order to solve the technical problems, the application provides a soft mirror clear imaging system for lithotripsy, which comprises:
the target area image acquisition module is used for: acquiring a gray level image of an imaging image to be cleaned, and dividing the gray level image to acquire a target area image;
the edge pixel point and non-edge pixel point acquisition module is used for: performing edge detection on the target area image, and determining each edge pixel point and each non-edge pixel point in the target area image;
a first target area enhanced image acquisition module for: according to the gray value of each non-edge pixel point in the target area image, gray balance processing is carried out on each non-edge pixel point, so that a first target area enhanced image is obtained;
a reference pixel point acquisition module, configured to: taking each edge pixel point in the first target area enhanced image as a central pixel point, and determining a sliding window area of the central pixel point and each reference pixel point in the sliding window area;
a second target area enhanced image acquisition module for: according to the gray value of each edge pixel point in the first target area enhanced image and the gray value of each reference pixel point in the sliding window area of each edge pixel point, gray value adjustment processing is carried out on each edge pixel point and each corresponding reference pixel point in the first target area enhanced image, so that a second target area enhanced image is obtained;
a clear imaging image acquisition module for: and determining a sharpened imaging image according to the second target area enhanced image and the imaging image.
Further, gray value adjustment processing is performed on each edge pixel point and each corresponding reference pixel point in the enhanced image of the first target area, so as to obtain an enhanced image of the second target area, which includes:
calculating the accumulated sum of the gray values of all the reference pixel points according to the gray values of all the reference pixel points in the sliding window area of each edge pixel point in the first target area enhanced image;
determining the ratio of the gray value of each edge pixel point in the first target area enhanced image to the corresponding accumulated sum, carrying out negative correlation mapping on the ratio, and determining the product of the negative correlation mapping result and the gray value of the corresponding edge pixel point as the adjusted gray value of the corresponding edge pixel point;
according to the gray value of each edge pixel point in the first target area enhanced image and the adjusted gray value, determining the gray adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image relative to each corresponding edge pixel point;
and adjusting the gray scale value of the reference pixel point in the first target area enhanced image according to the gray scale adjustment values of the reference pixel point in the first target area enhanced image relative to different edge pixel points, so as to obtain a second target area enhanced image.
Further, determining a gray scale adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image relative to each corresponding edge pixel point includes:
and calculating the difference value between the gray value of each edge pixel point in the first target area enhanced image and the gray value adjusted by the corresponding edge pixel point, and determining the ratio of the difference value to the number of each reference pixel point corresponding to the corresponding edge pixel point in the first target area enhanced image as the gray adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image.
Further, adjusting the gray value of the reference pixel point in the enhanced image of the first target area, thereby obtaining an enhanced image of the second target area, including:
and determining the gray value of the reference pixel point in the first target area enhanced image and the overlapping value of the gray adjustment value of the reference pixel point relative to different edge pixel points as the adjusted gray value of the reference pixel point in the first target area enhanced image, thereby obtaining a second target area enhanced image.
Further, gray level equalization processing is performed on each non-edge pixel point, so as to obtain a first target area enhanced image, which includes:
according to the gray values of all the non-edge pixel points in the target area image, determining the average value, the minimum value and the maximum value of the gray values of all the non-edge pixel points;
and updating the gray value of each non-edge pixel point in the target area image according to the average value, the minimum value and the maximum value, so as to obtain a first target area enhanced image.
Further, the gray value of each non-edge pixel point in the target area image is updated, so that a calculation formula corresponding to the enhanced image of the first target area is obtained, wherein the calculation formula comprises:
wherein F (x, y) is the first target region in the enhanced imageGray values of non-edge pixels at coordinates (x, y), f (x, y) is gray value of non-edge pixels at coordinates (x, y) in the target area image, μ is average value of gray values of all non-edge pixels in the target area image, S max S is the maximum value of gray values of all non-edge pixel points in the target area image min And the minimum value of gray values of all non-edge pixel points in the target area image is obtained.
Further, determining the sliding window area of the central pixel point and each reference pixel point in the sliding window area includes:
constructing a window with a set size taking the central pixel point as the center, and determining the window as a sliding window area of the central pixel point;
and determining the pixel points remained after the background pixel points, the edge pixel points and the corresponding central pixel points are removed from the sliding window area as each reference pixel point in the sliding window area.
Further, determining the sharpened imaged image includes:
and multiplying the second target area enhanced image and the imaging image to obtain a clear imaging image.
Further, the dividing the gray scale image to obtain a target area image includes:
performing binarization processing on the gray level image by using an Ojin algorithm so as to obtain a binary image;
and multiplying the binary image with the gray level image to obtain a target area image.
The application has the following beneficial effects: in the lithotripsy operation process, when the imaging image detected by the soft lens is required to be subjected to the sharpness enhancement treatment, a calculus area in the imaging image is acquired, so that a target area image is obtained. Because uneven gray distribution of pixel points in a stone region and weak contrast of an edge region are causes of image blurring, each edge pixel point and each non-edge pixel point in a target region image are determined, gray balance processing is carried out on the interior of the stone region according to gray values of the non-edge pixel points, gray adjustment enhancement is carried out on the edge region of the stone region according to gray values of the edge pixel points and gray values of reference pixel points around the edge pixel points on the basis, and finally a second target region enhanced image is obtained. In the process of acquiring the enhanced image of the second target area, the interior of the stone area and the edge of the stone area are respectively enhanced, so that the gray level distribution uniformity degree of pixel points in the stone area and the contrast of the stone edge area are effectively improved, interference areas such as a background area and the like are not enhanced in the enhancement process, and the enhancement effect of the image is ensured. And finally, based on the enhanced image of the second target area, a clear and clear imaging image can be obtained, and the problem that the conventional soft mirror image is poor in clear processing effect is solved.
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In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a lithotripsy soft-mirror clear imaging system according to an embodiment of the application;
FIG. 2 is a flow chart of a lithotripsy soft-mirror visualization method according to an embodiment of the application;
FIG. 3 is a gray scale image corresponding to an imaging image to be sharpened according to an embodiment of the present application;
FIG. 4 is a binary image corresponding to the gray scale image in FIG. 3 according to an embodiment of the present application;
fig. 5 is a schematic diagram of an edge pixel point of the target area image corresponding to the gray scale image in fig. 3 according to an embodiment of the present application.
Detailed Description
In order to further describe the technical means and effects adopted by the present application to achieve the preset purpose, the following detailed description is given below of the specific implementation, structure, features and effects of the technical solution according to the present application with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. In addition, all parameters or indices in the formulas referred to herein are values after normalization that eliminate the dimensional effects.
The embodiment provides a lithotripsy soft-lens clear imaging system, which consists of modules for realizing corresponding functions, and the corresponding structural schematic diagram is shown in fig. 1. The modules are mutually matched, and the aim is to realize a lithotripsy soft lens clear imaging method, and a corresponding flow chart of the method is shown in figure 2. The lithotripsy soft-mirror clear imaging system is described in detail below in combination with the functions realized by the respective modules.
As shown in fig. 1, the lithotripsy soft-mirror visualization imaging system comprises the following modules:
the target area image acquisition module is used for: and acquiring a gray level image of the imaging image to be subjected to sharpening processing, and dividing the gray level image to acquire a target area image.
In lithotripsy, a soft-mirror image detected by a soft-mirror is acquired and used as an imaging image to be subjected to sharpness processing. In order to perform sharpening processing on the imaging image to be sharpened, gray-scale processing is performed on the imaging image to be sharpened, so that a gray-scale image is obtained. Fig. 3 shows a gray scale image corresponding to a certain imaged image to be sharpened. Dividing the gray level image to obtain a target area image, namely: performing binarization processing on the gray level image by using an Ojin algorithm so as to obtain a binary image; and multiplying the binary image with the gray level image to obtain a target area image. The specific implementation process of processing the gray level image by using the Ojin algorithm to obtain a binarized image and extracting the target area in the gray level image by using the binarized image, thereby obtaining the corresponding target area image, belongs to the prior art, and is not repeated here. Fig. 4 shows a binary image corresponding to the gray image in fig. 3, in which the stone region corresponds to the white region and the background region corresponds to the black region.
The edge pixel point and non-edge pixel point acquisition module is used for: and carrying out edge detection on the target area image, and determining each edge pixel point and each non-edge pixel point in the target area image.
By analyzing the target area image and the gray level histogram corresponding to the target area image, it is known that the gray level value of the calculus area is mainly in the high gray level value area, the gray level value in the target area image is unevenly distributed, and the fact that the edge area is not prominent is the main cause of image blurring. Therefore, in order to make the imaged image clearer, the gray value of the image cannot be stretched over the whole gray interval by the original histogram equalization, but should be stretched over the original gray interval of the calculus region, even if the image of the target region is equalized in the characteristic gray interval, and the gray distribution in the characteristic gray interval is made more uniform; meanwhile, special treatment is needed to be carried out on the edge area of the image of the target area, namely, the edge area is treated after the whole image is balanced, so that the contrast of the edge area of the image of the target area is enhanced, and the edge area of the image is more obvious. The better image enhancement effect is realized by solving the problems of uneven gray value distribution and unobvious edge area, so that the image definition is realized.
In order to realize the sharpening process of the imaging image to be sharpened, a canny operator is used for carrying out edge detection on the target area image, so that each edge pixel point and each non-edge pixel point in the target area image are determined. The non-edge pixel points refer to pixels which do not belong to edge pixels in the pixels of the calculus region, and do not include background pixels. Considering that the boundary of the target region image is not obvious, the gradient parameter should be set smaller when the edge detection is performed on the target region image using the canny operator. The present embodiment sets the gradient parameter to 0.1. Fig. 5 shows a schematic diagram of edge pixels of the target area image corresponding to the gray level image in fig. 3, that is, the white portion in fig. 5 is the edge pixels of the calculus area.
A first target area enhanced image acquisition module for: and carrying out gray level equalization processing on each non-edge pixel point in the target area image according to the gray level value of each non-edge pixel point, so as to obtain a first target area enhanced image.
After determining each edge pixel point and each non-edge pixel point in the target area image, performing gray level equalization processing on each non-edge pixel point in the target area image, thereby obtaining a first target area enhanced image, wherein the implementation steps comprise:
according to the gray values of all the non-edge pixel points in the target area image, determining the average value, the minimum value and the maximum value of the gray values of all the non-edge pixel points;
and updating the gray value of each non-edge pixel point in the target area image according to the average value, the minimum value and the maximum value, so as to obtain a first target area enhanced image.
Specifically, according to the gray values of the non-edge pixels in the target area image, the non-edge pixels refer to pixels in the target area image except for the edge pixels and the black background outside the stone area, the average value of the gray values of the non-edge pixels is determined, and the minimum value and the maximum value of the gray values of the non-edge pixels are determined. Updating the gray value of each non-edge pixel point in the target area image according to the determined average value, minimum value and maximum value, thereby obtaining a first target area enhanced image, wherein the corresponding calculation formula is as follows:
wherein F (x, y) is the gray value of the non-edge pixel point at the coordinate (x, y) in the first target area enhanced image, namely the gray value after updating the non-edge pixel point at the coordinate (x, y) in the target area image, F (x, y) is the gray value of the non-edge pixel point at the coordinate (x, y) in the target area image, namely the gray value before updating the non-edge pixel point at the coordinate (x, y) in the first target area enhanced image, v is the average value of the gray values of all the non-edge pixel points in the target area image, S max S is the maximum value of gray values of all non-edge pixel points in the target area image min And the minimum value of gray values of all non-edge pixel points in the target area image is obtained.
The gray value of each non-edge pixel point in the target area image is updated in the above mode, namely, the average value of the gray values of the pixel points in the calculus area is calculated, the gray value of the pixel points in the calculus area is compared with the average value, and the length S of the original gray value interval is calculated max -S min The difference between the gray value of the pixel point in the calculus region and the average value is calculated to occupy the length S of the original gray value interval max -S min The gray value of the pixel point in the stone region is enhanced, the gray value distribution uniformity in the stone region can be improved, and the updated gray value interval is the same as the original gray value interval, namely the updated gray value still falls in S min ，S max ]Between them. When the gray value in the image of the target area is higher than the gray average value, the original gray value is weakened, and when the gray value is lower than the gray average value, the original gray value is enhanced, so that the gray homogenization degree in the calculus area is improved.
A reference pixel point acquisition module, configured to: and taking each edge pixel point in the first target area enhanced image as a central pixel point, and determining a sliding window area of the central pixel point and each reference pixel point in the sliding window area.
After homogenizing gray values of pixel points in a calculus region in a target region image to obtain a first target region enhanced image, in order to facilitate the subsequent improvement of contrast of edge pixel points of the calculus region in the target region image, for each edge pixel point in the first target region enhanced image, the edge pixel point refers to an edge pixel point at the same position in the target region image, the edge pixel point is taken as a central pixel point, a sliding window region of the central pixel point and each reference pixel point in the sliding window region are determined, namely a window with a set size taking the central pixel point as the center is constructed, and the window is determined as the sliding window region of the central pixel point; and determining the pixel points remained after the background pixel points, the edge pixel points and the corresponding central pixel points are removed from the sliding window area as each reference pixel point in the sliding window area. The size of the window may be set reasonably according to experiments or experience, and in this embodiment, the size of the window is set to 3*3. When each reference pixel point in the sliding window area is determined, the pixel points in the window area can be background pixel points or edge pixel points except for the corresponding center pixel point, so that the pixel gray values of the edge pixel points can be weakened according to the situation of surrounding pixel points conveniently and better, the gray points of the surrounding pixel points are strengthened, meanwhile, the interference of an irrelevant background area is avoided, and therefore the pixel points remained after the background pixel points, the corresponding center pixel points and other edge pixel points are removed from the window area are used as each reference pixel point.
A second target area enhanced image acquisition module for: and according to the gray value of each edge pixel point in the enhanced image of the first target area and the gray value of each reference pixel point in the sliding window area of each edge pixel point, carrying out gray value adjustment processing on each edge pixel point and each corresponding reference pixel point in the enhanced image of the first target area, thereby obtaining the enhanced image of the second target area.
In order to increase the contrast of the edge area of the calculus area in the enhanced image of the first target area, based on the gray value of each edge pixel point in the enhanced image of the first target area and the gray value of each reference pixel point in the sliding window area of each edge pixel point, gray value adjustment processing is performed on each edge pixel point and each corresponding reference pixel point in the enhanced image of the first target area, so as to obtain an enhanced image of the second target area, the implementation steps include:
calculating the accumulated sum of the gray values of all the reference pixel points according to the gray values of all the reference pixel points in the sliding window area of each edge pixel point in the first target area enhanced image;
determining the ratio of the gray value of each edge pixel point in the first target area enhanced image to the corresponding accumulated sum, carrying out negative correlation mapping on the ratio, and determining the product of the negative correlation mapping result and the gray value of the corresponding edge pixel point as the adjusted gray value of the corresponding edge pixel point;
according to the gray value of each edge pixel point in the first target area enhanced image and the adjusted gray value, determining the gray adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image relative to each corresponding edge pixel point;
and adjusting the gray scale value of the reference pixel point in the first target area enhanced image according to the gray scale adjustment values of the reference pixel point in the first target area enhanced image relative to different edge pixel points, so as to obtain a second target area enhanced image.
Specifically, for any one edge pixel point in the enhanced image of the first target area, according to the gray value of each reference pixel point in the sliding window area of the edge pixel point, the gray value of the edge pixel point is adjusted, and the calculation formula corresponding to the adjusted gray value is as follows:
wherein g' is the gray value of each edge pixel point in the enhanced image of the second target area, namely the firstThe gray value g of each edge pixel point in the target area enhanced image after adjustment 0 The gray value of each edge pixel point in the image is enhanced for the first target area, that is, the gray value before adjustment of each edge pixel point in the image is enhanced for the second target area,enhancing a cumulative sum, g, of gray values of respective reference pixel points in a sliding window region of each edge pixel point in the image for the first target region i For the gray value of the ith reference pixel in the sliding window area of each edge pixel in the first target area enhanced image, t is the total number of reference pixels in the sliding window area of each edge pixel in the first target area enhanced image,for the ratio->Is a negative correlation mapping result of (a).
According to the mode, gray value adjustment processing is carried out on each edge pixel point in the enhanced image of the first target area, namely the gray value of the edge pixel point is weakened according to the ratio of the gray value of the edge pixel point to the gray value accumulation of the reference pixel point in the sliding window area corresponding to the gray value adjustment processing, when the contrast ratio of the edge pixel point is larger, namely the ratio of the gray value of the edge pixel point to the sum of the gray values of the reference pixel points in the sliding window area is largerThe smaller the gray scale adjustment amount of the edge pixel point is, the smaller the contrast at the edge pixel point is, namely, the ratio of the gray scale value of the edge pixel point to the sum of the gray scale values of the reference pixel points in the sliding window area +.>The larger the gray scale adjustment amount of the edge pixel point is, the larger the gray scale adjustment amount is.
After gray value adjustment processing is carried out on each edge pixel point in the enhanced image of the first target area, the weakened gray value of each edge pixel point is evenly distributed to the reference pixel points in the sliding window area corresponding to the edge pixel point, so that gray of surrounding pixel points of the edge pixel point is enhanced, and the contrast of the edge area of the calculus can be better enhanced. At this time, for each edge pixel point in the enhanced image of the first target area, determining a gray adjustment value of each reference pixel point corresponding to the edge pixel point relative to each corresponding edge pixel point, where the implementation steps include:
and calculating the difference value between the gray value of each edge pixel point in the first target area enhanced image and the gray value adjusted by the corresponding edge pixel point, and determining the ratio of the difference value to the number of each reference pixel point corresponding to the corresponding edge pixel point in the first target area enhanced image as the gray adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image.
The calculation formula corresponding to the gray adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image relative to the corresponding edge pixel point is as follows:
wherein Δg i Enhancing gray scale adjustment values g of reference pixel points corresponding to the ith edge pixel point in the image for the first target area relative to the ith edge pixel point 0 For the gray value of the ith edge pixel in the enhanced image of the first target area, g' is the gray value of the ith edge pixel in the enhanced image of the first target area after adjustment, K is the total number of pixels in the sliding window area of the ith edge pixel in the enhanced image of the first target area, in this embodiment k= 9,N is the total number of irrelevant pixels in the sliding window area of the ith edge pixel in the enhanced image of the first target area, that is, the background pixel in the sliding window areaThe total number of the ith edge pixel point and other edge pixel points, and K-N represents the number of each reference pixel point corresponding to the ith edge pixel point in the enhanced image of the first target area.
According to the above manner of determining the gray adjustment value of each reference pixel point corresponding to the ith edge pixel point in the enhanced image of the first target area relative to the ith edge pixel point, for each edge pixel point in the enhanced image of the first target area, the gray change value corresponding to each edge pixel point is equally distributed to each corresponding reference pixel point, so as to obtain the gray adjustment value of each reference pixel point relative to the edge pixel point.
After determining the gray scale adjustment value of each reference pixel point corresponding to each edge pixel point in the enhanced image of the first target area, adjusting the gray scale value of the reference pixel point in the enhanced image of the first target area, thereby obtaining the enhanced image of the second target area, the implementation steps comprise:
and determining the gray value of the reference pixel point in the first target area enhanced image and the overlapping value of the gray adjustment value of the reference pixel point relative to different edge pixel points as the adjusted gray value of the reference pixel point in the first target area enhanced image, thereby obtaining a second target area enhanced image.
For any one reference pixel point in the enhanced image of the first target area, the calculation formula corresponding to the adjusted gray value is as follows:
wherein h' is the gray value of each reference pixel point in the enhanced image of the second target area, that is, the adjusted image gray value of each reference pixel point in the enhanced image of the first target area, h 0 The gray value before adjustment for each reference pixel point in the second target area enhanced image, namely the image gray value of each reference pixel point in the first target area enhanced image, delta g i For the first purposeAnd (3) adjusting the gray scale value of each reference pixel point in the target area enhanced image relative to the ith edge pixel point, wherein M is the total number of the edge pixel points relative to each reference pixel point in the first target area enhanced image.
The image gray value of each reference pixel point in the enhanced image of the first target area is adjusted in the mode, namely, the weakened gray value of the edge pixel point is equally distributed to each corresponding reference pixel point while the edge pixel point in the enhanced image of the first target area is weakened, the reference pixel points synthesize the gray values distributed by different edge pixel points, gray enhancement is carried out, so that the contrast of the edge area of the calculus is improved, and finally the enhanced image of the second target area is obtained.
The enhancement processing of the stone region is completed by carrying out gray value equalization on the non-edge region of the target region image and carrying out contrast enhancement processing on the edge region after the gray value equalization, and finally, a second target region enhanced image is obtained. In the whole process of acquiring the enhanced image of the second target area, only the stone area is selected for image enhancement, and the background area is not processed, so that the image enhancement effect is effectively ensured.
A clear imaging image acquisition module for: and determining a sharpened imaging image according to the second target area enhanced image and the imaging image.
After the second target area enhanced image is obtained, in order to achieve the purpose of enhancing the original image calculus area, the second target area enhanced image and the imaging image are multiplied, so that a clear imaging image is obtained, and the clear imaging image is a soft mirror image subjected to clear enhancement processing. In the soft-mirror image after the sharpness enhancement treatment, the stone region is more prominent, and the stone structure can be seen more clearly. The subsequent doctor can refer to the soft mirror image after the definition treatment to more clearly observe the calculus area in the human body, thereby improving the success rate and the efficiency of the operation.
It should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (9)
1. A lithotripsy soft-mirror visualization system, comprising:
the target area image acquisition module is used for: acquiring a gray level image of an imaging image to be cleaned, and dividing the gray level image to acquire a target area image;
the edge pixel point and non-edge pixel point acquisition module is used for: performing edge detection on the target area image, and determining each edge pixel point and each non-edge pixel point in the target area image;
a first target area enhanced image acquisition module for: according to the gray value of each non-edge pixel point in the target area image, gray balance processing is carried out on each non-edge pixel point, so that a first target area enhanced image is obtained;
a reference pixel point acquisition module, configured to: taking each edge pixel point in the first target area enhanced image as a central pixel point, and determining a sliding window area of the central pixel point and each reference pixel point in the sliding window area;
a second target area enhanced image acquisition module for: according to the gray value of each edge pixel point in the first target area enhanced image and the gray value of each reference pixel point in the sliding window area of each edge pixel point, gray value adjustment processing is carried out on each edge pixel point and each corresponding reference pixel point in the first target area enhanced image, so that a second target area enhanced image is obtained;
a clear imaging image acquisition module for: and determining a sharpened imaging image according to the second target area enhanced image and the imaging image.
2. The lithotripsy soft-mirror sharpness imaging system of claim 1, wherein the gray value adjustment process is performed on each edge pixel point and each corresponding reference pixel point in the first target region enhancement image, so as to obtain a second target region enhancement image, comprising:
calculating the accumulated sum of the gray values of all the reference pixel points according to the gray values of all the reference pixel points in the sliding window area of each edge pixel point in the first target area enhanced image;
determining the ratio of the gray value of each edge pixel point in the first target area enhanced image to the corresponding accumulated sum, carrying out negative correlation mapping on the ratio, and determining the product of the negative correlation mapping result and the gray value of the corresponding edge pixel point as the adjusted gray value of the corresponding edge pixel point;
according to the gray value of each edge pixel point in the first target area enhanced image and the adjusted gray value, determining the gray adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image relative to each corresponding edge pixel point;
and adjusting the gray scale value of the reference pixel point in the first target area enhanced image according to the gray scale adjustment values of the reference pixel point in the first target area enhanced image relative to different edge pixel points, so as to obtain a second target area enhanced image.
3. The lithotripsy soft-mirror visualization system of claim 2, wherein determining the gray scale adjustment value of each reference pixel corresponding to each edge pixel in the first target region enhanced image relative to each edge pixel comprises:
and calculating the difference value between the gray value of each edge pixel point in the first target area enhanced image and the gray value adjusted by the corresponding edge pixel point, and determining the ratio of the difference value to the number of each reference pixel point corresponding to the corresponding edge pixel point in the first target area enhanced image as the gray adjustment value of each reference pixel point corresponding to each edge pixel point in the first target area enhanced image.
4. The lithotripsy soft-mirror visualization system of claim 2, wherein adjusting the gray values of the reference pixels in the first target region-enhanced image to obtain a second target region-enhanced image comprises:
and determining the gray value of the reference pixel point in the first target area enhanced image and the overlapping value of the gray adjustment value of the reference pixel point relative to different edge pixel points as the adjusted gray value of the reference pixel point in the first target area enhanced image, thereby obtaining a second target area enhanced image.
5. The lithotripsy soft-mirror sharpness imaging system of claim 1, wherein gray-scale equalization of the non-edge pixels to obtain a first target area enhanced image, comprising:
according to the gray values of all the non-edge pixel points in the target area image, determining the average value, the minimum value and the maximum value of the gray values of all the non-edge pixel points;
and updating the gray value of each non-edge pixel point in the target area image according to the average value, the minimum value and the maximum value, so as to obtain a first target area enhanced image.
6. The lithotripsy soft-lens sharpness imaging system of claim 5, wherein the gray values of the non-edge pixels in the target area image are updated to obtain a calculation formula corresponding to the enhanced image of the first target area, where:
wherein F (x, y) is the gray value of the non-edge pixel point at the coordinate (x, y) in the first target area enhanced image, F (x, y) is the gray value of the non-edge pixel point at the coordinate (x, y) in the target area image, μ is the average value of the gray values of all the non-edge pixel points in the target area image, S max S is the maximum value of gray values of all non-edge pixel points in the target area image min And the minimum value of gray values of all non-edge pixel points in the target area image is obtained.
7. The lithotripsy soft-mirror visualization imaging system of claim 1, wherein determining a sliding window region of the center pixel and each reference pixel in the sliding window region comprises:
constructing a window with a set size taking the central pixel point as the center, and determining the window as a sliding window area of the central pixel point;
and determining the pixel points remained after the background pixel points, the edge pixel points and the corresponding central pixel points are removed from the sliding window area as each reference pixel point in the sliding window area.
8. The lithotripsy soft-mirror visualization system of claim 1, wherein determining the visualization image comprises:
and multiplying the second target area enhanced image and the imaging image to obtain a clear imaging image.
9. The lithotripsy soft-mirror visualization system of claim 1, wherein segmenting the gray scale image to obtain an image of a target region comprises:
performing binarization processing on the gray level image by using an Ojin algorithm so as to obtain a binary image;
and multiplying the binary image with the gray level image to obtain a target area image.
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Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005064861A (en) * | 2003-08-12 | 2005-03-10 | Minolta Co Ltd | Image processing apparatus |
| CN102024155A (en) * | 2010-12-06 | 2011-04-20 | 广州科易光电技术有限公司 | Rapid matching method of multispectral images based on edge detection |
| CN102044071A (en) * | 2010-12-28 | 2011-05-04 | 上海大学 | Single-pixel margin detection method based on FPGA |
| CN102158714A (en) * | 2010-02-11 | 2011-08-17 | 昆山锐芯微电子有限公司 | Method and device for enhancing image edge based on RGB (Red, Green and Blue) format of Bayer domain |
| CN103226829A (en) * | 2013-04-25 | 2013-07-31 | 广州中国科学院软件应用技术研究所 | Image edge detection method based on edge enhancement operator |
| CN104123697A (en) * | 2013-04-23 | 2014-10-29 | 华为技术有限公司 | Image enhancement method and device |
| CN105118067A (en) * | 2015-09-22 | 2015-12-02 | 成都融创智谷科技有限公司 | Image segmentation method based on Gaussian smoothing filter |
| CN107292834A (en) * | 2017-05-24 | 2017-10-24 | 杭州天铂红外光电技术有限公司 | Infrared image detail enhancing method |
| CN109146816A (en) * | 2018-08-22 | 2019-01-04 | 浙江大华技术股份有限公司 | A kind of image filtering method, device, electronic equipment and storage medium |
| WO2019071734A1 (en) * | 2017-10-12 | 2019-04-18 | 深圳市华星光电半导体显示技术有限公司 | Method for enhancing local contrast of image |
| WO2019200657A1 (en) * | 2018-04-16 | 2019-10-24 | 深圳市华星光电技术有限公司 | Method for processing image edge, electronic device, and computer readable storage medium |
| CN111833368A (en) * | 2020-07-02 | 2020-10-27 | 重庆邮电大学 | Voice restoration method based on phase consistency edge detection |
| CN111985329A (en) * | 2020-07-16 | 2020-11-24 | 浙江工业大学 | Remote sensing image information extraction method based on FCN-8s and improved Canny edge detection |
| CN112150368A (en) * | 2019-06-27 | 2020-12-29 | 北京金山云网络技术有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN113596573A (en) * | 2021-07-28 | 2021-11-02 | Oppo广东移动通信有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN217181459U (en) * | 2022-04-29 | 2022-08-12 | 广州医科大学附属第四医院(广州市增城区人民医院) | Medical type expiration date reckoning rotary table |
| CN114998355A (en) * | 2022-08-08 | 2022-09-02 | 江苏京德新材料有限公司 | Production defect identification method and device for sealing rubber ring |
| CN115272346A (en) * | 2022-09-30 | 2022-11-01 | 江苏炜盛光电科技有限公司 | PCB production process online detection method based on edge detection |
| CN115311282A (en) * | 2022-10-12 | 2022-11-08 | 南通迪博西电子有限公司 | Wafer defect detection method based on image enhancement |
| CN115330645A (en) * | 2022-10-17 | 2022-11-11 | 南通惠尔风机有限公司 | Welding image enhancement method |
| CN115345893A (en) * | 2022-09-15 | 2022-11-15 | 吉林大学中日联谊医院 | Ovarian tissue canceration region segmentation method based on image processing |
| CN116152261A (en) * | 2023-04-24 | 2023-05-23 | 济南奥盛包装科技有限公司 | Visual inspection system for quality of printed product |
| CN116168039A (en) * | 2023-04-26 | 2023-05-26 | 济宁市新华电力特种材料有限公司 | Environment-friendly energy-saving aluminum silicate plate quality detection method |
| CN116309579A (en) * | 2023-05-19 | 2023-06-23 | 惠州市宝惠电子科技有限公司 | Transformer welding seam quality detection method using image processing |
| CN116363133A (en) * | 2023-06-01 | 2023-06-30 | 无锡斯达新能源科技股份有限公司 | Illuminator accessory defect detection method based on machine vision |
-
2023
- 2023-07-04 CN CN202310817137.5A patent/CN116883270B/en active Active
Patent Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005064861A (en) * | 2003-08-12 | 2005-03-10 | Minolta Co Ltd | Image processing apparatus |
| CN102158714A (en) * | 2010-02-11 | 2011-08-17 | 昆山锐芯微电子有限公司 | Method and device for enhancing image edge based on RGB (Red, Green and Blue) format of Bayer domain |
| CN102024155A (en) * | 2010-12-06 | 2011-04-20 | 广州科易光电技术有限公司 | Rapid matching method of multispectral images based on edge detection |
| CN102044071A (en) * | 2010-12-28 | 2011-05-04 | 上海大学 | Single-pixel margin detection method based on FPGA |
| CN104123697A (en) * | 2013-04-23 | 2014-10-29 | 华为技术有限公司 | Image enhancement method and device |
| CN103226829A (en) * | 2013-04-25 | 2013-07-31 | 广州中国科学院软件应用技术研究所 | Image edge detection method based on edge enhancement operator |
| CN105118067A (en) * | 2015-09-22 | 2015-12-02 | 成都融创智谷科技有限公司 | Image segmentation method based on Gaussian smoothing filter |
| CN107292834A (en) * | 2017-05-24 | 2017-10-24 | 杭州天铂红外光电技术有限公司 | Infrared image detail enhancing method |
| WO2019071734A1 (en) * | 2017-10-12 | 2019-04-18 | 深圳市华星光电半导体显示技术有限公司 | Method for enhancing local contrast of image |
| WO2019200657A1 (en) * | 2018-04-16 | 2019-10-24 | 深圳市华星光电技术有限公司 | Method for processing image edge, electronic device, and computer readable storage medium |
| CN109146816A (en) * | 2018-08-22 | 2019-01-04 | 浙江大华技术股份有限公司 | A kind of image filtering method, device, electronic equipment and storage medium |
| CN112150368A (en) * | 2019-06-27 | 2020-12-29 | 北京金山云网络技术有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN111833368A (en) * | 2020-07-02 | 2020-10-27 | 重庆邮电大学 | Voice restoration method based on phase consistency edge detection |
| CN111985329A (en) * | 2020-07-16 | 2020-11-24 | 浙江工业大学 | Remote sensing image information extraction method based on FCN-8s and improved Canny edge detection |
| CN113596573A (en) * | 2021-07-28 | 2021-11-02 | Oppo广东移动通信有限公司 | Image processing method, image processing device, electronic equipment and computer readable storage medium |
| CN217181459U (en) * | 2022-04-29 | 2022-08-12 | 广州医科大学附属第四医院(广州市增城区人民医院) | Medical type expiration date reckoning rotary table |
| CN114998355A (en) * | 2022-08-08 | 2022-09-02 | 江苏京德新材料有限公司 | Production defect identification method and device for sealing rubber ring |
| CN115345893A (en) * | 2022-09-15 | 2022-11-15 | 吉林大学中日联谊医院 | Ovarian tissue canceration region segmentation method based on image processing |
| CN115272346A (en) * | 2022-09-30 | 2022-11-01 | 江苏炜盛光电科技有限公司 | PCB production process online detection method based on edge detection |
| CN115311282A (en) * | 2022-10-12 | 2022-11-08 | 南通迪博西电子有限公司 | Wafer defect detection method based on image enhancement |
| CN115330645A (en) * | 2022-10-17 | 2022-11-11 | 南通惠尔风机有限公司 | Welding image enhancement method |
| CN116152261A (en) * | 2023-04-24 | 2023-05-23 | 济南奥盛包装科技有限公司 | Visual inspection system for quality of printed product |
| CN116168039A (en) * | 2023-04-26 | 2023-05-26 | 济宁市新华电力特种材料有限公司 | Environment-friendly energy-saving aluminum silicate plate quality detection method |
| CN116309579A (en) * | 2023-05-19 | 2023-06-23 | 惠州市宝惠电子科技有限公司 | Transformer welding seam quality detection method using image processing |
| CN116363133A (en) * | 2023-06-01 | 2023-06-30 | 无锡斯达新能源科技股份有限公司 | Illuminator accessory defect detection method based on machine vision |
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