Underwater image restoration through a combination of improved dark channel prior and gray world algorithms

Xiao-ming, MA; Chen, Zhehan; Feng, Zhipeng

doi:10.1117/1.jei.28.5.053033

Cited by 13 publications

(10 citation statements)

References 23 publications

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“…To evaluate the proposed algorithm, the test underwater images are selected from multiple authoritative image datasets [22]. Qualitative and quantitative comparisons are conducted against five advanced underwater image enhancement algorithms, which includes the classical underwater dark channel prior algorithm (UDCP) [4], Zhang's multi-scale Retinex extension algorithm [6], Garg's contrast histogram equalization algorithm [7], Ma's combination of dark channel prior and gray world algorithm [8], and Islam's fast underwater image enhancement for improved visual perception algorithm (FUnIE-GAN) [11].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Underwater Image Enhancement Based on Color Balance and Multi-Scale Fusion

Chen

Zhang

2022

IEEE Photonics J.

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Underwater light absorption and scattering lead to color deviation, low brightness, fuzzy details and low contrast of underwater images. In this contribution, an underwater image enhancement algorithm based on color balance and multi-scale fusion is proposed. Firstly, a color balance method is used to correct the image color. Then, based on an improved dark channel prior algorithm, the local contrast information of the color balanced image is used to derive two atmospheric lights and transmission maps, which are transformed into the images with enhanced contrast and brightness, respectively. Finally, the multi-scale fusion method is adopted to fuse the contrast enhanced image and the brightness improved image according to the weights. The proposed algorithm is compared with other underwater image enhancement algorithms in qualitative and quantitative evaluation. Experimental results show that the proposed algorithm can effectively eliminate color deviation, remove dark areas, and improve brightness and contrast of underwater images. The enhanced images generated by the proposed algorithm are superior to those generated by other algorithms in indicators of PSNR, SSIM, UIQM and UCIQE. Thus, the proposed algorithm is suitable for underwater image enhancement.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Nevertheless, it fails to remove the color deviation of images. Ma et al [8] proposed a combination of the dark channel prior method and the gray world method to defog underwater image, but the brightness of obtained images is still low.…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Enhancement Based on Color Balance and Multi-Scale Fusion

Chen

Zhang

2022

IEEE Photonics J.

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“…For example, Shin et al [8] used the convolution networks to estimate ambient light and realized underwater image colour correction based on physical model. Codruta et al [9] estimated the light attenuation according to the red channel, and then locally adjusted the pixel values of other colour channels by using the colour transfer function, so as to enhance the underwater image. Jamadandi et al [10] corrected the distorted image colour using the wavelength correction depth convolution network parameters.…”

Section: Related Theoriesmentioning

confidence: 99%

“…The proposed method has better FIGURE 8 The comparison of the visual results of all methods on bluish underwater images. From left to right are raw images, and the results of red-channel [25], GDCP [29], MLFcGAN [31], BICE [26], ATMG [27], UWCNN [28], MLSD [30], BR [32], MTGM [33], Our method FIGURE 9 The comparison of the visual results of all methods on greenish underwater images. From left to right are raw images, and the results of red-channel [25], GDCP [29], MLFcGAN [31], BICE [26], ATMG [27], UWCNN [28], MLSD [30], BR [32], MTGM [33], Our method FIGURE 10 The comparison of the visual results of all methods on shallow underwater images.…”

Section: Experiments On Real Datasetsmentioning

confidence: 99%

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Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Shi

Wang

Zhou

et al. 2022

IET Image Processing

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Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods.

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