CN110689587A - Underwater image enhancement method based on color correction and detail enhancement - Google Patents
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Abstract
The invention provides an underwater image enhancement method based on color correction and detail enhancement. The underwater image enhancement method comprises the following two steps: color correction and detail enhancement. Firstly, selecting a part of clear underwater images, and referring to the average value of the clear images Lab to adjust the value of the underwater images Lab to be restored so as to realize color correction. And aiming at the color correction image, converting an RGB space into an HSV space, carrying out histogram equalization on H, and carrying out normalization processing on S and V to realize contrast enhancement. And secondly, processing the linear combination of the contrast-enhanced images by adopting a Laplacian operator to obtain an edge mapping map, and performing linear weighting fusion with the contrast-enhanced images and the edge mapping map to obtain the finally enhanced underwater image. The algorithm realizes color correction by adjusting the Lab space value, and realizes detail enhancement by using the Laplacian operator, so that the image has rich detail information on the basis of color correction, and the integral visual effect of the image is improved.
Description
Technical Field
The invention relates to an image enhancement method, in particular to an underwater image enhancement method based on color correction and detail enhancement.
Background
Since the ocean contains abundant resources, the development and protection of ocean resources are increasingly concerned. The underwater image is an important carrier of ocean information, and the acquisition of the underwater image is closely related to an underwater imaging system. The underwater image has important application value in a plurality of research fields such as marine biology, archaeology, underwater surveying and mapping, underwater exploration, underwater target detection and the like. The quality of the collected image is reduced due to scattering and absorption of the underwater collected image. Scattering is the collision of light with suspended particles, the direction of propagation changes, resulting in image blurring. Absorption is the absorption of light by the suspended particles, resulting in a bluish green image. In order to improve the visual effect of the image and present more underwater material resource information, the underwater image definition increasingly becomes a research hotspot.
The underwater image processing aims to improve the visual effect of an underwater image, improve the contrast of the image and solve color cast. The underwater image processing technology is generally divided into two main categories, underwater image enhancement and restoration. The underwater image enhancement method does not need to consider an underwater image degradation mechanism, improves the visual effect of an image by processing pixels, and generally comprises histogram equalization, white balance, a Retinex-based method and an image fusion-based method. The underwater image restoration method restores images through an imaging model, the key parameters of the underwater image restoration method are transmitted light and ambient light estimation, and the method which is usually adopted is a priori-based method and a depth learning-based method.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an underwater image enhancement method based on color correction and detail enhancement. The method is based on the color correction and detail enhancement underwater image enhancement method, and the Lab value of the image to be enhanced is adjusted by referring to the Lab value of the clear image to obtain a color correction chart. And performing HSV space conversion on the image after color correction, performing histogram equalization on the H component, performing normalization operation on the S and V components to realize image contrast enhancement, realizing edge detail enhancement by using a Laplacian operator, and finally performing linear weighting and fusion on the contrast enhancement image and the edge mapping graph to obtain the enhanced underwater image. The method can realize underwater image enhancement and simultaneously ensure color and detail, effectively improve the visual effect of the image, improve the definition of the image, and can be recommended to be applied to the field of underwater image processing.
The technical scheme adopted by the invention is as follows: the invention comprises an underwater image enhancement method based on color correction and detail enhancement, which is characterized by further comprising the following steps of:
step S01: inputting an original image, and converting the original image from an RGB space into a Lab space;
step S02: selecting 9 visual clear underwater images in a public data set, respectively calculating Lab values of the 9 clear underwater images, solving the mean value of the Lab values, and setting the mean value of the Lab values as a reference value;
step S03: adjusting the Lab value of the original image according to the reference value set in the step S02, performing color correction, and acquiring a color-corrected image;
step S04: converting the color-corrected image acquired in the step S03 into an HSV space;
step S05: processing the components of the HSV space obtained in the step S04, performing histogram equalization on the hue component H, performing normalization on the saturation component S and the hue component V, enhancing the contrast, and obtaining an image with enhanced contrast;
step S06: performing laplacian pyramid filtering on the linear combination of the contrast-enhanced images obtained in the step S05 to obtain an edge map;
step S07: and performing linear weighted fusion on the contrast-enhanced image obtained in the step S05 and the edge map obtained in the step S06 to obtain an enhanced image.
Further, in the step S01, the RGB space is converted to the Lab space, and the original image needs to be converted from the RGB space to the LMS color space first, the theoretical formula is:
where R, G, and B denote channel values of the original image in RGB space, and L, M, and S denote channel values converted to LMS space, respectively. Since the data is relatively scattered in the LMS space, it is further converted into a logarithmic space.
Where L ', M ', S ' respectively represent channel values of the LMS space.
Further, the Lab space in step S01 is obtained by the following method, and the obtaining formula is:
wherein L represents the L component in the Lab color space, α represents the a component in the Lab color space, and β represents the b component in the Lab color space.
Further, the Lab space in step S01 is converted into the RGB color space, and the formula is defined as:
wherein, in the Lab color space, L represents a luminance channel, and a and b each represent a color channel; the a color channel displays a red to green gradient from the minimum to the maximum, and the b color channel displays a yellow to blue gradient from the minimum to the maximum; the luminance channel L has a value range of [0,100], and a and b have a value range of [ -128,127 ].
Further, screening 9 underwater images with clear vision from the public data set, converting the underwater images into Lab color space, calculating the average value of an L channel, an a channel and a b channel, and obtaining the L-39.84 through calculation; a is-0.5686; b is-4.0969, and the value is used as a reference value of the Lab space value of the clear underwater image;
the color-distorted underwater image is converted to Lab color space, and then the a-channel is adjusted to-0.5686 and the b-channel is adjusted to-4.0969, and a color-adjusted underwater image is obtained.
Further, the color components of the HSV space obtained in step S04 are processed respectively, histogram equalization is performed on the hue component H, normalization is performed on the saturation component S and the hue component V, and the color-adjusted image contrast is enhanced:
Ich＝histeq(Icoh)；
wherein IcohH component, Ic representing the result of color correctionhThe H component representing the contrast enhancement result.
Further, the laplacian in step S06 performs detail enhancement on the color-improved image edge, wherein an alpha parameter in laplacian is set to 0.2, and the laplacian w is defined as:
the edge map IE is defined as:
IE＝w*(Ic+(1-Ic)*Ic)；
where Ic denotes the contrast enhancement result and IE denotes the edge map.
Further said enhanced image is defined as:
wherein IenhanceShows the enhanced results.
Compared with the prior art, the invention has the following advantages:
aiming at the problems of color distortion and poor contrast of the underwater image, the method disclosed by the invention realizes the underwater image enhancement by adopting a color correction and detail enhancement method. Adjusting the Lab color space value to realize color correction and solve the color cast problem; and converting the space into an HSV color space, carrying out histogram equalization on the H component, carrying out normalization operation on the S and V components, realizing image contrast enhancement, and realizing image edge detail enhancement by utilizing a Laplacian operator. And finally, linearly weighting and fusing the contrast enhancement image and the edge mapping image to realize image contrast enhancement.
Based on the reason, the method can be popularized and applied in the fields of underwater image processing and the like.
Drawings
In order to clarify the invention or the technical solution, the drawings to be used for the description of the embodiments or the prior art will be briefly summarized below.
FIG. 1 is a flow chart embodying the present invention;
FIG. 2 is a comparison graph of the enhancement effect of the invention on an underwater coral scene image compared with other algorithms. Wherein FIG. 2-1 represents an original image; FIG. 2-2 shows an SSR algorithm effect graph; FIGS. 2-3 show MSR algorithm effect graphs; FIGS. 2-4 show the effect of the MSRCR method after treatment; FIGS. 2-5 show the effect of the method of the present invention after treatment.
FIG. 3 is a comparison graph of the enhancement effect of the underwater fish swarm scene image according to the invention and other algorithms. Wherein FIG. 3-1 represents an original image; FIG. 3-2 shows an SSR algorithm effect graph; 3-3 show MSR algorithm effect graphs; FIGS. 3-4 show the effect of the MSRCR method after treatment; FIGS. 3-5 show the effect of the method of the present invention after treatment.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to verify the effectiveness of image enhancement, the images in the open data set of the underwater scene are selected as a test data set, and compared, analyzed and verified with an SSR algorithm, an MSR algorithm, an MSRCR algorithm and the experimental results of the method in subjective and objective aspects. The specific steps and principles are as follows:
as shown in FIG. 1, the invention provides an underwater image enhancement method based on color correction and detail enhancement, which comprises the following steps: step S01: inputting an original image, and converting the original image from an RGB space into a Lab space;
step S02: selecting 9 visual clear underwater images in a public data set, respectively calculating Lab values of the 9 clear underwater images, solving the mean value of the Lab values, and setting the mean value of the Lab values as a reference value;
step S03: adjusting the Lab value of the original image according to the reference value set in the step S02, performing color correction, and acquiring a color-corrected image;
step S04: converting the color-corrected image acquired in the step S03 into an HSV space;
step S05: processing the components of the HSV space obtained in the step S04, performing histogram equalization on the hue component H, performing normalization on the saturation component S and the hue component V, enhancing the contrast, and obtaining an image with enhanced contrast;
step S06: performing laplacian pyramid filtering on the linear combination of the contrast-enhanced images obtained in the step S05 to obtain an edge map;
step S07: and performing linear weighted fusion on the contrast-enhanced image obtained in the step S05 and the edge map obtained in the step S06 to obtain an enhanced image.
As a preferred embodiment, in the present application, the RGB space is converted to the Lab space in step S01, and the original image needs to be converted from the RGB space to the LMS color space, the theoretical formula is:
where R, G, and B denote channel values of the original image in RGB space, and L, M, and S denote channel values converted to LMS space, respectively. Since the data is relatively scattered in the LMS space, it is further converted into a logarithmic space.
Where L ', M ', S ' respectively represent channel values of the LMS space.
As a preferred embodiment, the Lab space in step S01 is obtained by the following method:
wherein L represents the L component in the Lab color space, α represents the a component in the Lab color space, and β represents the b component in the Lab color space.
In the present application, as a preferred embodiment, the Lab space in step S01 is converted into the RGB color space, and the formula is defined as:
wherein, in the Lab color space, L represents a luminance channel, and a and b each represent a color channel; the a color channel displays a red to green gradient from the minimum to the maximum, and the b color channel displays a yellow to blue gradient from the minimum to the maximum; the luminance channel L has a value range of [0,100], and a and b have a value range of [ -128,127 ].
In the embodiment, 9 visually clear underwater images are screened from the public data set, converted into a Lab color space, and the average value of an L channel, an a channel and a b channel is calculated, so that L is 39.84; a is-0.5686; b is-4.0969, and the value is used as a reference value of the Lab space value of the clear underwater image;
the color-distorted underwater image is converted to Lab color space, and then the a-channel is adjusted to-0.5686 and the b-channel is adjusted to-4.0969, and a color-adjusted underwater image is obtained.
As a preferred embodiment, the color components of the HSV space obtained in step S04 are processed separately, histogram equalization is performed on the hue component H, normalization is performed on the saturation component S and the hue component V, and the color adjustment image contrast is enhanced:
Ich＝histeq(Icoh)；
wherein IcohH component, Ic representing the result of color correctionhRepresenting contrast enhancementThe resulting H component.
In this application, it is preferable that the laplacian in step S06 performs detail enhancement on the color-improved image edge, where an alpha parameter in laplacian is set to 0.2, and the laplacian w is defined as:
the edge map IE is defined as:
IE＝w*(Ic+(1-Ic)*Ic)；
where Ic denotes the contrast enhancement result and IE denotes the edge map.
In this application, a preferred embodiment of the enhanced image is defined as:
wherein IenhanceShows the enhanced results.
Examples
As shown in FIG. 2, an experimental effect graph after the underwater coral scene is processed by other algorithms is provided for the invention. The effect graph after experimental enhancement can obviously show that the method and other methods have certain enhancement effect, improve the overall contrast of the image and enhance the local detail information. And analyzing the effect graph, wherein the coral images enhanced by the SSR algorithm, the MSR algorithm and the MSRCR algorithm have a color cast phenomenon. The method of the invention effectively enhances the contrast of the image and has rich colors. The coral treated by the method has clear foreground texture, rich colors and obviously enhanced contrast. Therefore, the method can effectively enhance the image details and restore the color.
As shown in fig. 3, an experimental effect graph after processing an underwater fish school scene with other algorithms is provided for the invention. The effect graph after experimental enhancement can obviously show that the method and other methods have certain enhancement effect, improve the overall contrast of the image and enhance the local detail information. And analyzing the fish school images enhanced by the SSR algorithm, the MSR algorithm and the MSRCR algorithm from the effect map, and showing color deviation. The fish school edge processed by the method is clearer, and the contrast is obviously enhanced. Therefore, the method can effectively enhance the image details and restore the color.
The AG value in table 1 is an average gradient, which indicates image detail information, and the larger the evaluation index value is, the clearer the image is, and the better the image enhancement effect is. Two scene images of an underwater public data set are selected for verification, and the AG value of the image processed by the method is higher than that of other comparison methods, so that the enhancement effect of the method is better than that of other algorithms.
TABLE 1 average gradient comparison of the results of the inventive and other algorithms
| Serial number | Original drawing | SSR | MSR | MSRCR | Algorithm of the invention |
| FIG. 2 | 4.503 | 5.042 | 5.042 | 5.102 | 10.500 |
| FIG. 3 | 2.542 | 4.482 | 4.483 | 3.001 | 11.557 |
The UIQM in table 2 is a non-reference underwater color image quality evaluation method, and the larger the evaluation index value is, the better the balance among color, definition and contrast of the image is represented, indicating that the image enhancement effect is better. Two scene images of an underwater public data set are selected for verification, and the UIQM value of the image processed by the method is higher than that of other comparison methods, so that the enhancement effect of the method is better than that of other algorithms.
TABLE 2 UIQM comparison of processing results of the inventive algorithm and other algorithms
| Serial number | Original drawing | SSR | MSR | MSRCR | Algorithm of the invention |
| FIG. 2 | 2.933 | 3.693 | 3.734 | 3.273 | 5.077 |
| FIG. 3 | 0.577 | 3.842 | 3.839 | 1.272 | 4.412 |
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the unit may be a logical function division, and there may be another division manner in actual implementation.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention 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 solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. An underwater image enhancement method based on color correction and detail enhancement is characterized by comprising the following steps:
step S01: inputting an original image, and converting the original image from an RGB space into a Lab space;
step S02: selecting 9 visual clear underwater images in a public data set, respectively calculating Lab values of the 9 clear underwater images, solving the mean value of the Lab values, and setting the mean value of the Lab values as a reference value;
step S03: adjusting the Lab value of the original image according to the reference value set in the step S02, performing color correction, and acquiring a color-corrected image;
step S04: converting the color-corrected image acquired in the step S03 into an HSV space;
step S05: processing the components of the HSV space obtained in the step S04, performing histogram equalization on the hue component H, performing normalization on the saturation component S and the hue component V, enhancing the contrast, and obtaining an image with enhanced contrast;
step S06: performing laplacian pyramid filtering on the linear combination of the contrast-enhanced images obtained in the step S05 to obtain an edge map;
step S07: and performing linear weighted fusion on the contrast-enhanced image obtained in the step S05 and the edge map obtained in the step S06 to obtain an enhanced image.
2. An underwater image enhancement method based on color correction and detail enhancement according to claim 1, further characterized by: in the step S01, the RGB space is converted to the Lab space, and the original image needs to be converted from the RGB space to the LMS color space, where the theoretical formula is:
wherein, R, G, B represent the channel value of the original image in RGB space, L, M, S represent the channel value converted to LMS space separately; since the data is more dispersed in the LMS space, it is further converted to the log space:
where L ', M ', S ' respectively represent channel values of the LMS space.
3. An underwater image enhancement method based on color correction and detail enhancement according to claim 1, further characterized by: the Lab space in step S01 is obtained by the following method, and the obtaining formula is:
wherein L represents the L component in the Lab color space, α represents the a component in the Lab color space, and β represents the b component in the Lab color space.
4. An underwater image enhancement method based on color correction and detail enhancement according to claim 1, further characterized by: the Lab space in step S01 is converted into the RGB color space, and the formula is defined as:
wherein, in the Lab color space, L represents a luminance channel, and a and b each represent a color channel; the a color channel displays a red to green gradient from the minimum to the maximum, and the b color channel displays a yellow to blue gradient from the minimum to the maximum; the luminance channel L has a value range of [0,100], and a and b have a value range of [ -128,127 ].
5. An underwater image enhancement method based on color correction and detail enhancement according to claim 1, further characterized by: screening 9 underwater images with clear vision from the public data set, converting the underwater images into Lab color space, calculating the average value of an L channel, an a channel and a b channel, and obtaining the L-39.84 through calculation; a is-0.5686; b is-4.0969, and the value is used as a reference value of the Lab space value of the clear underwater image;
the color-distorted underwater image is converted to Lab color space, and then the a-channel is adjusted to-0.5686 and the b-channel is adjusted to-4.0969, and a color-adjusted underwater image is obtained.
6. An underwater image enhancement method based on color correction and detail enhancement according to claim 1, further characterized by: processing the color components of the HSV space obtained in the step S04, performing histogram equalization on the hue component H, performing normalization on the saturation component S and the hue component V, and enhancing the color adjustment image contrast:
Ich＝histeq(Icoh)；
wherein IcohH component, Ic representing the result of color correctionhThe H component representing the contrast enhancement result.
7. An underwater image enhancement method based on color correction and detail enhancement according to claim 1, further characterized by: the laplacian in step S06 performs detail enhancement on the color improved image edge, wherein the alpha parameter in laplacian is set to 0.2, and the laplacian w is defined as:
the edge map IE is defined as:
IE＝w*(Ic+(1-Ic)*Ic)；
where Ic denotes the contrast enhancement result and IE denotes the edge map.
8. The underwater image enhancement method based on color correction and detail enhancement as claimed in claim 1, wherein the enhanced image is defined as:
wherein IenhanceShows the enhanced results.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170116712A1 (en) * | 2015-10-21 | 2017-04-27 | Chunghwa Picture Tubes, Ltd. | Image enhancement method and image processing apparatus thereof |
| CN107507138A (en) * | 2017-07-27 | 2017-12-22 | 北京大学深圳研究生院 | A kind of underwater picture Enhancement Method based on Retinex model |
| 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 |
| CN110175964A (en) * | 2019-05-30 | 2019-08-27 | 大连海事大学 | A kind of Retinex image enchancing method based on laplacian pyramid |
-
2019
- 2019-10-11 CN CN201910961751.2A patent/CN110689587B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170116712A1 (en) * | 2015-10-21 | 2017-04-27 | Chunghwa Picture Tubes, Ltd. | Image enhancement method and image processing apparatus thereof |
| CN107507138A (en) * | 2017-07-27 | 2017-12-22 | 北京大学深圳研究生院 | A kind of underwater picture Enhancement Method based on Retinex model |
| WO2019019695A1 (en) * | 2017-07-27 | 2019-01-31 | 北京大学深圳研究生院 | Underwater image enhancement method based on retinex model |
| 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 |
| CN110175964A (en) * | 2019-05-30 | 2019-08-27 | 大连海事大学 | A kind of Retinex image enchancing method based on laplacian pyramid |
Non-Patent Citations (2)
| Title |
|---|
| 李昌利等: "基于多通道均衡化的水下彩色图像增强算法", 《华中科技大学学报(自然科学版)》 * |
| 蔡利梅等: "自适应HSV空间Retinex煤矿监控图像增强算法", 《电视技术》 * |
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