Deep Color Compensation for Generalized Underwater Image Enhancement

Rao, Yuan; Liu, Wenjie; Li, Kunqian; Fan, Hao; Wang, Sen; Dong, Junyu

doi:10.1109/tcsvt.2023.3305777

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…The generator is used to generate an image close to the actual image and the discriminator should not be able to distinguish the generated image. Rao et al [20] proposed a probabilistic colour compensation network. This approach is performed in two stages, where the colour compensation if performed first and then enhances the image.…”

Section: Colour Constancy-based Methodsmentioning

confidence: 99%

“…Finally, we convert the XYZ values to CIELAB color space using equations (2)-( 4). Introduced YIQ and HIS colour space High complexity and cannot remove noise Mean, Contrast, Entropy, MSE, PSNR Zhang et al [10] Reduces noise and improves details It cannot enhance the image contrast UIQM, UCIQE, Entropy Tang et al [14] Intensity channel introduced to MSR Processing and filtering techniques are inefficient -Huang et al [15] Improves natural colour reproduction Unable to dehaze the image effectively UIQM, PSNR Rao et al [20] Developed a probabilistic colour compensation network Unable to remove the colour casts present in the image UCIQE, UIQM, FDUM Zhang et al [19] Produces colour accurate images Unable to dehaze the image effectively PSNR, SSIM, FSIM, UIQM, UCIQE Now, once the RGB image is converted into CIELAB colour space, we then perform white balancing on the A and B channel to remove the effect of colour cast from the image. Then, we estimate the average colour and then compare it to the gray colour.…”

Section: Removing Colour Castsmentioning

confidence: 99%

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Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Dua,

Nalawade,

Dua

2024

Phys. Scr.

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Underwater images can be captured either with the help of light waves or sound waves. Images that are taken underwater typically are not of optimum quality as they suffer from issues such as low contrast, blurring of detail, colour distortion, and greenish tones. Several physical processes that take place in the aquatic environment, such as light absorption, refraction, and scattering, are responsible for the existence of such degradation in underwater images. To address these challenges, numerous researchers have put forth a range of cutting-edge techniques for enhancing and restoring such degraded underwater images, with the aim of addressing these issues. These techniques primarily focus on improving visibility and enhancing the level of detail. To achieve this, we propose a method that performs White Balancing in the LAB colour space to remove the bluish-greenish tones present in the image. Next, we enhance the contrast by first converting the RGB image into HSV (Hue, Saturation, Value) and HLS (Hue, Lightness, and Saturation) colour spaces and then by using the S & V channels in HSV and L & S colour channels in HLS, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE). To control the brightness of the enhanced image, we apply Gamma Correction. Lastly, by using the method Dark Channel Prior (DCP), we separate the image’s red channel from the RGB colour space and perform the dehazing operation to get the final enhanced image. We have conducted a comprehensive qualitative analysis of our proposed approach as well as existing techniques, evaluating them objectively and subjectively through metrics such as Peak Signal-to-Noise Ratio (PSNR), Root-Mean-Square Error (RMSE), Structural Similarity (SSIM), and the Underwater Colour Image Quality Evaluation metric (UCIQE).

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Section: Colour Constancy-based Methodsmentioning

confidence: 99%

Section: Removing Colour Castsmentioning

confidence: 99%

Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Dua,

Nalawade,

Dua

2024

Phys. Scr.

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Underwater image enhancement via histogram similarity-oriented color compensation complemented by multiple attribute adjustment

Wang,

Frery,

et al. 2023

Intell. Mar. Technol. Syst.

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Underwater images are often influenced by color casts, low contrast, and blurred details. We observe that images taken in natural settings typically have similar histograms across color channels, while underwater images do not. To improve the natural appearance of an underwater image, it is critical to improve the histogram similarity across its color channels. To address this problem, we develop a histogram similarity-oriented color compensation method that corrects color casts by improving the histogram similarity across color channels in the underwater image. In addition, we apply the multiple attribute adjustment method, including max-min intensity stretching, luminance map-guided weighting, and high-frequency edge mask fusion, to enhance contrast, saturation, and sharpness, effectively addressing problems of low contrast and blurred details and eventually enhancing the overall appearance of underwater images. Particularly, the method proposed in this work is not based on deep learning, but it effectively enhances a single underwater image. Comprehensive empirical assessments demonstrated that this method exceeds state-of-the-art underwater image enhancement techniques. To facilitate public assessment, we made our reproducible code available at https://gitee.com/wanghaoupc/UIE_HS2CM2A.

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Underwater variable zoom: Depth-guided perception network for underwater image enhancement

Huang,

Wang,

et al. 2025

Expert Systems with Applications

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Deep Color Compensation for Generalized Underwater Image Enhancement

Cited by 6 publications

References 53 publications

Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Underwater image enhancement via histogram similarity-oriented color compensation complemented by multiple attribute adjustment

Underwater variable zoom: Depth-guided perception network for underwater image enhancement

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