CN114638765A - Low-illumination image enhancement method based on complementary gamma conversion - Google Patents
Low-illumination image enhancement method based on complementary gamma conversion Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention discloses a low-illumination image enhancement method based on complementary gamma conversion, which comprises the following steps: (1) converting an original color image from an RGB space to an HSV space, and acquiring an illumination component V, a hue component H and a saturation component S of the image; (2) processing the illumination component V by adopting a complementary gamma transformation function to obtain an enhanced illumination component V‘(ii) a (3) And then converting the color image from the HSV space to the RGB space to obtain an enhanced image. The invention can effectively improve the image blurring phenomenon caused by uneven illumination, so that the visual effect of the image is better, and the high exposure part of the image is inhibited.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a low-illumination image enhancement method based on complementary gamma conversion.
Background
Due to the limitations of image acquisition technology, imaging environment and other factors, it is sometimes very difficult to obtain high-quality images, and images taken under extreme weather conditions or at night often have low visibility, blurred details and greatly reduced quality. Obtaining an image with low illumination is almost unavoidable. Therefore, it is necessary to enhance the low-illuminance image to meet our needs. In the prior art, the image is enhanced by adopting a weighted distribution adaptive gamma correction enhancement map (AGCWD) or a low-light-level image enhancement map (LIME) technology for illumination estimation, but some high-exposure parts exist in the image enhanced by adopting the above technology.
Disclosure of Invention
The purpose of the invention is as follows: in view of the above disadvantages, the present invention provides a low-illumination image enhancement method based on complementary gamma conversion, which can effectively improve the image blurring caused by uneven illumination, so that the visual effect of the image is better, and at the same time, the high-exposure part of the image is suppressed.
The technical scheme is as follows: in order to solve the above problems, the present invention provides a low illumination image enhancement method based on complementary gamma transformation, comprising the following steps:
(1) converting an original color image from an RGB space to an HSV space, and acquiring an illumination component V, a hue component H and a saturation component S of the image;
(2) processing the illumination component V by adopting a complementary gamma transformation function to obtain an enhanced illumination component V'; the formula of the complementary gamma transformation function is as follows:
V′＝a1V1+a2V2
V1＝Vr
V2＝1-(1-V)r
wherein r is 2.2; a is1、a2Are all weight coefficients;
(3) and then converting the color image from the HSV space to the RGB space to obtain an enhanced image.
Further, the weight coefficient calculation formula in step (2) is as follows:
in the formula: i, taking 1 and 2;
represents ViAverage value of (a).
Further, the formula for converting the original color image from the RGB space to the HSV space is as follows:
further, the formula for converting the color image from HSV space to RGB space is:
wherein: h isi＝[H/60]mod6,f＝H/60-hi，p＝V′×(1-S)，q＝V′×(1-f×S),t＝V′×(1-(1-f)×S)。
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the designed complementary gamma conversion correction function is used for processing the illumination component V of the image, so that the brightness distribution of the image is more uniform, the details of the image are effectively enhanced, and the visual quality of the image is higher; and the brightness of the whole enhanced image is improved, and simultaneously, the high exposure part of the image is restrained, and the details are enhanced to a certain extent.
Drawings
FIG. 1 is a flow chart of a method according to the present invention;
FIG. 2 is a graph illustrating image contrast enhancement; FIG. 2(a) shows an original drawing; FIG. 2(b) shows an enhancement of the present invention;
FIG. 3 is a graph showing the comparison of the enhancement results of various algorithms; fig. 3(a) shows an original, fig. 3(b) shows an enhancement map of Adaptive Gamma Correction (AGCWD) using a weight distribution, fig. 3(c) shows an enhancement map of low-light-level image enhancement map (LIME) using illumination estimation, and fig. 3(d) shows an enhancement map of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, the method for enhancing low-illumination images based on complementary gamma conversion according to the present invention specifically includes the following steps:
(1) converting an original color image from an RGB space to an HSV space, and acquiring an illumination component V, a hue component H and a saturation component S of the image; the concrete formula is as follows:
in the formula:
(2) the acquired illumination component V is processed using the designed complementary gamma correction function:
(a) the stretching formula for the V component uses a conventional gamma correction function, and the formula is:
V1＝Vr
(b) the designed compensation formula for the V component is as follows:
V2＝1-(1-V)r
(c) the complementary gamma correction function is designed as:
V′＝a1V1+a2V2
wherein r is 2.2; a is1、a2Are all weight coefficients; in order to enable the algorithm to be adaptive and the value to be maintained at 0,1]In the interval, the designed weight coefficient calculation formula is as follows:
wherein: i, taking 1 and 2;
represents ViAverage value of (a).
(3) And then converting the color image from the HSV space to the RGB space to obtain an enhanced image. The concrete formula is as follows:
wherein: h isi＝[H/60]mod6,f＝H/60-hi，p＝V′×(1-S)，q＝V′×(1-f×S),t＝V′×(1-(1-f)×S)。
In order to verify the effectiveness of the algorithm, a plurality of image tests are adopted to carry out comparison tests on the images before and after enhancement. As shown in fig. 2(a), the original image has the characteristics of blur, uneven illumination, and the like; as shown in fig. 2(b), after the image enhancement processing is performed by the method of the present invention, the image is clear, the image brightness is more uniform, and the enhancement effect is significant compared with the original image. As shown in fig. 3(a), the original image has the characteristics of blurring and uneven illumination, and as shown in fig. 3(b) and 3(c), although the overall brightness of the image is improved, the image has some high-exposure portions, as shown in fig. 3(d), and the method of the present invention can improve the overall brightness of the image, and simultaneously can inhibit the high-exposure portions of the image, and certain details are enhanced.
Claims (4)
1. A low-illumination image enhancement method based on complementary gamma conversion is characterized by comprising the following steps:
(1) converting an original color image from an RGB space to an HSV space, and acquiring an illumination component V, a hue component H and a saturation component S of the image;
(2) processing the illumination component V by adopting a complementary gamma transformation function to obtain an enhanced illumination component V'; the formula of the complementary gamma transformation function is as follows:
V′＝a1V1+a2V2
V1＝Vr
V2＝1-(1-V)r
wherein r is 2.2; a is1、a2Are all weight coefficients;
(3) and then converting the color image from the HSV space to the RGB space to obtain an enhanced image.
2. The method of low-illumination image enhancement based on complementary gamma conversion as claimed in claim 1, wherein the weight coefficient calculation formula in step (2) is as follows:
in the formula: i, taking 1 and 2;
represents ViAverage value of (a).
3. The complementary gamma transform-based low-illumination image enhancement method according to claim 1, wherein the formula for converting the original color image from the RGB space to the HSV space is as follows:
4. the complementary gamma transform-based low-illumination image enhancement method according to claim 1, wherein the formula for converting the color image from HSV space to RGB space is:
wherein: h isi＝[H/60]mod6,f＝H/60-hi，p＝V′×(1-S)，q＝V′×(1-f×S),t＝V′×(1-(1-f)×S)。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106504212A (en) * | 2016-11-07 | 2017-03-15 | 湖南源信光电科技有限公司 | A kind of improved HSI spatial informations low-luminance color algorithm for image enhancement |
| CN106530250A (en) * | 2016-11-07 | 2017-03-22 | 湖南源信光电科技有限公司 | Low illumination color image enhancement method based on improved Retinex |
| CN110706172A (en) * | 2019-09-27 | 2020-01-17 | 郑州轻工业学院 | Low-illumination color image enhancement method based on adaptive chaotic particle swarm optimization |
| CN111861899A (en) * | 2020-05-20 | 2020-10-30 | 河海大学 | Image enhancement method and system based on illumination nonuniformity |
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- 2022-03-30 CN CN202210325152.3A patent/CN114638765A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106504212A (en) * | 2016-11-07 | 2017-03-15 | 湖南源信光电科技有限公司 | A kind of improved HSI spatial informations low-luminance color algorithm for image enhancement |
| CN106530250A (en) * | 2016-11-07 | 2017-03-22 | 湖南源信光电科技有限公司 | Low illumination color image enhancement method based on improved Retinex |
| CN110706172A (en) * | 2019-09-27 | 2020-01-17 | 郑州轻工业学院 | Low-illumination color image enhancement method based on adaptive chaotic particle swarm optimization |
| CN111861899A (en) * | 2020-05-20 | 2020-10-30 | 河海大学 | Image enhancement method and system based on illumination nonuniformity |
Non-Patent Citations (1)
| Title |
|---|
| 智宁等: "基于双伽马函数的煤矿井下低亮度图像增强算法", 辽宁工程技术大学学报（自然科学版）, vol. 37, no. 1, 15 February 2018 (2018-02-15), pages 1 - 4 * |
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