CN110796612A - Image enhancement method and system - Google Patents
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Abstract
The invention discloses an image enhancement method and system, wherein the method comprises the following steps: firstly, converting a medical image from an RGB color space to a YUV color space to obtain a brightness component, calculating an improved image enhancement template based on G-L fractional order differential, then respectively performing first image enhancement and second image enhancement on the brightness of the image by adopting a fixed order enhancement template and a self-adaptive order enhancement template, and performing image fusion on the results of the two image enhancements to obtain a new brightness component; and finally, combining the new brightness component and the original chrominance information to obtain a new YUV color image, and then converting the YUV color image into an RGB color space to obtain a final enhanced medical image. The problem that texture details are unclear in the prior art is solved, the texture details are enhanced, and the image quality is improved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an image enhancement method and system.
Background
In recent years, with the development of computer technology, imaging technology is widely applied to clinical fields of modern medicine, such as magnetic resonance technology, computed tomography technology, and the like, and the medical imaging technology provides favorable conditions for diagnosis of doctors. However, the medical image is affected by noise during the formation process, resulting in the captured medical image having characteristics of blur, low contrast, etc., which make it difficult for a doctor to accurately obtain key information with naked eyes, thereby resulting in missed detection or false detection. Therefore, the enhancement processing of the medical image has very important application value.
Currently, common medical image texture structure enhancement methods mainly include sharpening enhancement, fuzzy set enhancement, multi-scale geometric enhancement, enhancement methods based on differential operators and the like. For different medical image imaging characteristics, various methods have advantages and disadvantages, for example, sharpening enhancement can enhance small details and edge parts of an image, but is sensitive to noise; although the fuzzy set enhancement can process a large amount of uncertainty in the medical image to obtain a good enhancement effect, the fuzzy set enhancement needs to rely on more priori knowledge and has low robustness; multi-scale geometric enhancement can be realized by performing targeted enhancement on an image at multiple scales, but easily generating a ringing effect; the reinforcement based on the differential operator can effectively process structures such as angular points, tubular structures and the like, but can not well solve the problem of the grammatical details in the smooth region in the detected image, so that the image quality is not high.
Disclosure of Invention
The invention provides an image enhancement method and system, which are used for overcoming the defects that the texture in an image is not clear enough and the like in the prior art, and enhancing the texture details of the image so as to improve the image processing quality.
In order to achieve the above object, the present invention provides an image enhancement method, comprising the steps of:
converting an image to be processed from an RGB color space to a YUV color space to obtain brightness information and chrominance information;
constructing an image enhancement basic template in a two-dimensional function based on a G-L fractional order differential operator;
improving the image enhancement basic template according to the correlation between the pixels and the central pixels to obtain an image enhancement template;
filtering the brightness information by adopting an image enhancement template with a fixed order number to obtain a primary image enhancement result;
filtering the brightness information by adopting an image enhancement template with a self-adaptive order to obtain a secondary image enhancement result;
fusing the primary image intensity result and the secondary image intensity result to obtain final brightness information;
and converting the new YUV color space image obtained by combining the final brightness information and the chrominance information into an RGB color space to obtain an enhanced image.
In order to achieve the above object, the present invention further provides an image enhancement system, which includes a memory and a processor, wherein the memory stores an image enhancement program, and the processor executes the steps of any one of the above methods when the processor runs the image enhancement program.
The image enhancement method and the image enhancement system provided by the invention carry out color space conversion on the collected image to be processed to obtain brightness information and chrominance information; solving the difference of the two-dimensional image function according to the G-L fractional order differential operator to construct an image enhancement basic template for detecting the image edge; when fractional order differentiation is applied to image processing, the image enhancement templates with fixed orders and self-adaptive orders are combined, the distances and the correlations between other pixels and central pixels are considered, compared with the traditional image enhancement template with fixed orders, texture detail information with small gray level change in an image smooth region is not greatly recorded and is reserved to a certain extent, corresponding processing can be performed on a texture region and a smooth region with abundant texture, the edge of an image interested region is highlighted, and more edge detail information can be extracted; then, weighting and fusing the two image enhancement results to obtain final brightness information; finally, reducing the YUV color space image according to the final brightness information and the chrominance information, and converting the YUV color space image into an RGB color space to obtain an enhanced image; and the texture details of the image are enhanced, and the image processing quality is improved.
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 of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a flowchart of an image enhancement method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
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.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; the connection can be mechanical connection, electrical connection, physical connection or wireless communication connection; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
Example one
As shown in fig. 1, an embodiment of the present invention provides an image enhancement method, which is described in detail below by taking enhancement of a medical image as an example, and mainly includes the following steps:
s1, converting the image to be processed from RGB color space to YUV color space to obtain brightness information and chroma information;
the image to be processed is a medical image, but can also be applied to a palm print image, a pupil image, a human face image, a natural image and the like, but is not limited to the medical image, firstly, a medical RGB image to be processed is input, and the image to be processed is sampled at a fixed frequency;
converting the image to be processed from the RGB color space to the YUV color space by the following conversion formula:
in the YUV color space, the luminance information Y and the two chrominance information U, V are completely separated. The value ranges of the parameters are as follows: r is a red coefficient: 0 to 255; g is the green coefficient: 0 to 255; b is a blue coefficient: 0-255. The parameter values are also called tristimulus coefficients or primary color coefficients or color values, and are divided by 255 and normalized to 0-1; the transformation space is used for converting colors into gray values so as to facilitate calculation, and is a common means in the image processing process;
s2, constructing an image enhancement basic template in a two-dimensional function based on a G-L fractional order differential operator;
the difference of fractional order differentiation of the corresponding two-dimensional image function on the x axis and the y axis is solved by setting the gray value of the image as f (x, y), wherein (why a convolution template is written, which is not shown in other papers) the difference of the G-L fractional order differentiation of the one-dimensional function f (x) is expanded on the two-dimensional function f (x, y); wherein, the value range of the one-dimensional function f (x) is the difference format of G-L fractional order differential of [ a, b ]:
where v is the fractional order, n ═ [ (b-a)/h ], h is the unit step size value 1, [ a ] denotes the rounding of a, Γ (n) ═ n-1! (ii) a
Expanding the formula (2) to a two-dimensional function f (x, y), respectively carrying out fractional order differential processing in the x direction and the y direction, and selecting the first three coefficients to construct a 5 x 5G-L fractional order differential image enhancement basic template:
wherein c1 ═ 8, c2 ═ -v, and c3 ═ v (v)2-v)/2；
Finally, using c as 8-12v +4v2G0 is normalized to obtain the final enhanced template
S3, improving the image enhancement basic template according to the correlation between the pixel and the central pixel to obtain an image enhancement template;
and improving the image enhancement basic template by adopting the distance correlation between the Gaussian template representation pixel and the central pixel to obtain the image enhancement template.
Conventional fractional order differential image enhancement assumes that the correlation of each pixel with the center pixel is the same. In fact, there is some relationship between the correlation between the image pixels and the distance from the central pixel, i.e., the correlation with the central pixel becomes lower as the distance from the central pixel increases. Therefore, when applying fractional order differentiation for image processing, the distances and correlations of other pixels and the center pixel should be considered. The Gaussian template can well represent the distance correlation between the pixel and the central pixel, so that the original template is improved, and the formula is as follows:
where Gauss is a 5 × 5 gaussian template, and · × represents a dot product operation between matrices. Then, the final enhanced template G1' can be obtained by carrying out normalization processing on G1.
S4, filtering the brightness information by adopting an image enhancement template with a fixed order number to obtain a primary image enhancement result;
filtering the brightness component Y of the image by adopting a fixed order and an enhancement template G1' to obtain a first-time image enhancement result Y1;
Y1＝Y*G1′ (5)
the fractional order v used in the template is a fixed value of 0.8, so that a good image enhancement effect can be obtained;
s5, filtering the brightness information by adopting an image enhancement template with a self-adaptive order to obtain a secondary image enhancement result;
s5 includes:
s51, obtaining the gradient module value of each pixel in the preset multi-direction according to the preset neighborhood region of the pixel and the brightness value of the brightness information in the pixel;
s52, obtaining the maximum gradient module value in multiple directions, and obtaining the fraction order corresponding to each pixel according to the maximum gradient value of the pixel;
s53, obtaining a corresponding image enhancement template according to the relative fractional order of each pixel; and obtaining a secondary image enhancement result of each pixel point.
The traditional fractional order differential image enhancement adopts a fixed order for processing, but the difference of different areas of an image is large, the included characteristics are different, the defect exists in the simple adoption of the same differential order for an image, and the enhancement effect is limited to a certain extent. The image gradient is the main characteristic reflecting the image space transformation rate, so that the corresponding order of each pixel is calculated based on the gradient information, and then the pixel is enhanced according to the result to obtain a second image enhancement result.
(1) Calculating a gradient modulus value T (x, y) of each pixel;
in order to reflect the change situation of the brightness amplitude between the central pixel point and the neighborhood pixel point in more detail, gradient modes are solved in 8 directions of the pixel point, and the calculation formula is as follows:
T(x,y)＝max(|Y(x′,y′)-Y(x,y)|) (6)
wherein, (x ', Y') belongs to Ω (x, Y), Ω (x, Y) represents the 3 × 3 domain interval of the pixel (x, Y), and Y (x, Y) represents the brightness value of the brightness component Y at the pixel (x, Y);
(2) counting the maximum gradient module value, and recording as T max;
(3) calculating the corresponding fractional order v2(x, y) according to the gradient modulus T (x, y) of each pixel,
wherein, p is exp (T (x, y)/Tmax), and q is a value of 1.1 in the adjusting parameter, so that gradient information of a pixel point can be better represented;
(4) for each pixel (x, y), the corresponding fractional order v2(x, y) is adopted, formula (3) and formula (4) are substituted, a corresponding enhancement template is calculated, and is marked as G1' _2(x, y), and then the pixel is enhanced:
Y2(x,y)＝YΩ5(x,y)*G1′_2(x,y) (8)
wherein, YΩ5(x, Y) represents the sub-image in which the 5 × 5 domain interval of the pixel point (x, Y) on the luminance component Y is located, Y2(x, Y) represents the enhancement result of the pixel point (x, Y), and after all pixels are calculated, the secondary image enhancement result Y2 can be obtained;
s6, fusing the primary image enhancement result and the secondary image enhancement result to obtain final brightness information;
performing image fusion on the two enhancement results to obtain a final brightness component newY;
newY＝k1×Y1+(1-k1)×Y2 (9)
where k1 is a weighting factor, here taken to be 0.4, the results of the two image enhancements can be better fused.
And S7, converting the new YUV color space image obtained by combining the final brightness information and the chrominance information into an RGB color space to obtain an enhanced image. The calculation in step S1 is the inverse operation.
The medical image enhancement method provided by the invention can effectively enhance the texture details and improve the image quality, has low algorithm complexity, and can well meet the requirement of real-time processing.
Example two
Based on the first embodiment, an embodiment of the present invention further provides an image enhancement system, which includes a memory and a processor, where the memory stores an image enhancement program, and the processor executes the steps of any of the above-mentioned embodiments of the image enhancement method when running the image enhancement program.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An image enhancement method, comprising:
converting an image to be processed from an RGB color space to a YUV color space to obtain brightness information and chrominance information;
constructing an image enhancement basic template in a two-dimensional function based on a G-L fractional order differential operator;
improving the image enhancement basic template according to the correlation between the pixels and the central pixels to obtain an image enhancement template;
filtering the brightness information by adopting an image enhancement template with a fixed order number to obtain a primary image enhancement result;
filtering the brightness information by adopting an image enhancement template with a self-adaptive order to obtain a secondary image enhancement result;
fusing the primary image intensity result and the secondary image intensity result to obtain final brightness information;
and converting the new YUV color space image obtained by combining the final brightness information and the chrominance information into an RGB color space to obtain an enhanced image.
2. The image enhancement method of claim 1, wherein the step of transforming the image to be processed from an RGB color space to a YUV color space to obtain luminance information and chrominance information comprises:
collecting an image to be processed;
converting the image to be processed from the RGB color space to the YUV color space by the following conversion formula:
in the YUV color space, the luminance information Y and the two chrominance information U, V are completely separated.
3. The image enhancement method of claim 1, wherein the step of constructing an image enhancement base template based on a G-L fractional order differential in a two-dimensional function comprises:
expanding on a two-dimensional function f (x, y) according to a differential format of fractional G-L order differential of the one-dimensional function f (x); wherein, the value range of the one-dimensional function f (x) is the difference format of G-L fractional order differential of [ a, b ]:
where v is the fractional order, n ═ [ (b-a)/h ], h is the unit step size value 1, [ a ] denotes the rounding of a, Γ (n) ═ n-1! (ii) a
Expanding the formula (2) to a two-dimensional function f (x, y), respectively carrying out fractional order differential processing in the x direction and the y direction, and selecting the first three coefficients to construct a 5 x 5G-L fractional order differential image enhancement basic template:
wherein c1 ═ 8, c2 ═ -v, and c3 ═ v (v)2-v)/2；
Finally, using c as 8-12v +4v2G0 is normalized to obtain the final enhanced template
4. The image enhancement method of claim 3, wherein the step of improving the image enhancement basic template according to the correlation between the pixel and the central pixel, and obtaining the image enhancement template comprises:
and improving the image enhancement basic template by adopting the distance correlation between the Gaussian template representation pixel and the central pixel to obtain the image enhancement template.
5. The image enhancement method of claim 4, wherein the step of improving the image enhancement basic template by using the distance correlation between the Gaussian template representation pixel and the central pixel to obtain the image enhancement template comprises:
and performing the following dot product operation on the Gaussian template and the image enhancement basic template:
gauss is a 5 × 5 gaussian template, x represents a dot product operation between matrices;
and carrying out normalization processing on the point multiplication operation result G1 to obtain an image enhancement template G1'.
6. The image enhancement method of claim 5, wherein the step of filtering the luminance information using a fixed-order image enhancement template to obtain a primary image enhancement result comprises:
filtering the brightness information Y by adopting an image enhancement template G1' to obtain a primary image enhancement result Y1;
Y1＝Y*G1′ (5)
wherein the fractional order v used in the template takes a fixed value of 0.8.
7. The image enhancement method of claims 1 to 6, wherein the step of filtering the luminance information by using an image enhancement template of an adaptive order to obtain a secondary image enhancement result comprises:
acquiring a gradient module value of each pixel in a preset multi-direction according to a preset neighborhood region of the pixel and the brightness value of the brightness information in the pixel;
obtaining maximum gradient module values in multiple directions, and obtaining a fraction order corresponding to each pixel according to the maximum gradient value of the pixel;
obtaining a corresponding image enhancement template according to the relative fractional order of each pixel; and obtaining a secondary image enhancement result of each pixel point.
8. The image enhancement method according to claim 7, wherein the step of obtaining the gradient mode value of each pixel in the predetermined multiple directions from the predetermined neighborhood region of the pixel and the luminance information at the luminance value of the pixel comprises:
solving gradient modules in 8 directions of the pixel points, wherein the calculation formula is as follows:
T(x,y)＝max(|Y(x′,y′)-Y(x,y)|) (6)
wherein, (x ', Y') belongs to Ω (x, Y), Ω (x, Y) represents the 3 × 3 domain interval of the pixel (x, Y), and Y (x, Y) represents the brightness value of the brightness component Y at the pixel (x, Y);
the step of obtaining the maximum gradient module values in multiple directions and obtaining the fraction order corresponding to each pixel according to the maximum gradient value of the pixel comprises the following steps:
counting the maximum gradient module value, and recording as Tmax;
calculating the fraction order v2(x, y) corresponding to each pixel according to the gradient module value T (x, y) of each pixel and the maximum gradient module value:
wherein, p is exp (T (x, y)/Tmax), q is an adjusting parameter, and the value is 1.1;
obtaining a corresponding image enhancement template according to the relative fractional order of each pixel; the step of obtaining the secondary image enhancement result of each pixel point comprises the following steps:
for each pixel (x, y), taking the fractional order v2(x, y) corresponding to the pixel, obtaining an image enhancement template corresponding to the pixel according to equations (3) and (4), which is denoted as G1' _2(x, y), and then enhancing the pixel:
Y2(x,y)＝YΩ5(x,y)*G1′_2(x,y) (8)
wherein, YΩ5(x, Y) represents the sub-image in which the 5 × 5 domain interval of the pixel point (x, Y) is located on the luminance component Y, Y2(x, Y) represents the enhancement result of the pixel point (x, Y), and after all pixels are calculated, the secondary image enhancement result Y2 is obtained.
9. The image enhancement method of any one of claims 1 to 6, wherein the step of fusing the primary image enhancement result Y1 and the secondary image enhancement result Y2 to obtain final brightness information comprises:
carrying out image fusion on the two image enhancement results to obtain a final brightness component newY;
newY＝k1×Y1+(1-k1)×Y2 (9)
where k1 is a weighting factor, here taken to be 0.4.
10. An image enhancement system comprising a memory and a processor, wherein the memory stores an image enhancement program, and the processor executes the steps of the method of any one of claims 1 to 9 when running the image enhancement program.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112102204A (en) * | 2020-09-27 | 2020-12-18 | 苏州科达科技股份有限公司 | Image enhancement method and device and electronic equipment |
| CN112435195A (en) * | 2020-12-02 | 2021-03-02 | 湖南优象科技有限公司 | Image enhancement method and system based on adaptive fractional order differential |
| CN112634144A (en) * | 2020-11-20 | 2021-04-09 | 深圳市优象计算技术有限公司 | Medical image enhancement method and system based on fractional order differential |
| CN112907460A (en) * | 2021-01-25 | 2021-06-04 | 宁波市鄞州区测绘院 | Remote sensing image enhancement method |
| CN115511755A (en) * | 2022-11-22 | 2022-12-23 | 杭州雄迈集成电路技术股份有限公司 | Video stream image self-adaptive enhancement method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750680A (en) * | 2012-06-30 | 2012-10-24 | 四川农业大学 | Tea image enhancement method based on empirical mode decomposition and color space |
| CN103440623A (en) * | 2013-08-02 | 2013-12-11 | 中北大学 | Method for improving image definition in foggy days based on imaging model |
| CN106910215A (en) * | 2017-03-15 | 2017-06-30 | 沈阳理工大学 | A kind of super-resolution method based on fractional order gradient interpolation |
| CN107316279A (en) * | 2017-05-23 | 2017-11-03 | 天津大学 | Low light image Enhancement Method with regularization model is mapped based on tone |
| CN108564547A (en) * | 2018-04-19 | 2018-09-21 | 南京信息工程大学 | A kind of fractional order differential image enchancing method of adaptive differential order |
| CN108665431A (en) * | 2018-05-16 | 2018-10-16 | 南京信息工程大学 | Fractional order image texture Enhancement Method based on K- mean clusters |
| CN109191390A (en) * | 2018-08-03 | 2019-01-11 | 湘潭大学 | A kind of algorithm for image enhancement based on the more algorithm fusions in different colours space |
-
2019
- 2019-10-09 CN CN201910955851.4A patent/CN110796612B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750680A (en) * | 2012-06-30 | 2012-10-24 | 四川农业大学 | Tea image enhancement method based on empirical mode decomposition and color space |
| CN103440623A (en) * | 2013-08-02 | 2013-12-11 | 中北大学 | Method for improving image definition in foggy days based on imaging model |
| CN106910215A (en) * | 2017-03-15 | 2017-06-30 | 沈阳理工大学 | A kind of super-resolution method based on fractional order gradient interpolation |
| CN107316279A (en) * | 2017-05-23 | 2017-11-03 | 天津大学 | Low light image Enhancement Method with regularization model is mapped based on tone |
| CN108564547A (en) * | 2018-04-19 | 2018-09-21 | 南京信息工程大学 | A kind of fractional order differential image enchancing method of adaptive differential order |
| CN108665431A (en) * | 2018-05-16 | 2018-10-16 | 南京信息工程大学 | Fractional order image texture Enhancement Method based on K- mean clusters |
| CN109191390A (en) * | 2018-08-03 | 2019-01-11 | 湘潭大学 | A kind of algorithm for image enhancement based on the more algorithm fusions in different colours space |
Non-Patent Citations (4)
| Title |
|---|
| BO LO ET AL.: ""Adaptive fractional differential approach and its application to medical image enhancement"", 《ELSEVIER》 * |
| JINGANG CAO: ""An Image Enhancement Method Based on Fractional Calculus and Retinex"", 《JOURNAL OF COMPUTER AND COMMUNICATIONS》 * |
| 罗丽红: ""图像后处理增强算法的研究与实现"", 《中国优秀博硕士学位论文全文数据库（硕士） 信息科技辑》 * |
| 胡伏原 等: ""自适应分数阶微分的符合双边滤波算法"", 《中国图象图形学报》 * |
Cited By (9)
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|---|---|---|---|---|
| CN112102204A (en) * | 2020-09-27 | 2020-12-18 | 苏州科达科技股份有限公司 | Image enhancement method and device and electronic equipment |
| WO2022062346A1 (en) * | 2020-09-27 | 2022-03-31 | 苏州科达科技股份有限公司 | Image enhancement method and apparatus, and electronic device |
| CN112634144A (en) * | 2020-11-20 | 2021-04-09 | 深圳市优象计算技术有限公司 | Medical image enhancement method and system based on fractional order differential |
| CN112435195A (en) * | 2020-12-02 | 2021-03-02 | 湖南优象科技有限公司 | Image enhancement method and system based on adaptive fractional order differential |
| CN112435195B (en) * | 2020-12-02 | 2024-06-04 | 湖南优象科技有限公司 | Image enhancement method and system based on self-adaptive fractional order differentiation |
| CN112907460A (en) * | 2021-01-25 | 2021-06-04 | 宁波市鄞州区测绘院 | Remote sensing image enhancement method |
| CN112907460B (en) * | 2021-01-25 | 2022-07-29 | 宁波市鄞州区测绘院 | Remote sensing image enhancement method |
| CN115511755A (en) * | 2022-11-22 | 2022-12-23 | 杭州雄迈集成电路技术股份有限公司 | Video stream image self-adaptive enhancement method and system |
| CN115511755B (en) * | 2022-11-22 | 2023-03-10 | 杭州雄迈集成电路技术股份有限公司 | Video stream image self-adaptive enhancement method and system |
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