Underwater Image Restoration Based on Background Light Estimation and Dark Channel Prior

Haoling, 谢昊伶 Xie; Guohua, 彭国华 Peng; Fan, 王凡 Wang; Cheng, 杨成 Yang

doi:10.3788/aos201838.0101002

Cited by 8 publications

(4 citation statements)

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“…The algorithm worked under two ideal assumptions that there was uniform illumination and only forward scattering, which rarely occur in real world. In 2018, Xie et al [32] restored the image by first estimating the underwater optical parameters related to natural underwater light, and then calculating the transmittance of R, G and B channel respectively according to the relationship between scattering coefficient and wavelength of light. This type of method usually made some assumptions and hypotheses that were not always true in real underwater environment.…”

Section: Related Workmentioning

confidence: 99%

Underwater image clarifying based on human visual colour constancy using double‐opponency

Kong

Qian

Song

et al. 2023

CAAI Trans on Intel Tech

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Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water. Such images with degradation cannot meet the needs of underwater operations. The main problem in classic underwater image restoration or enhancement methods is that they consume long calculation time, and often, the colour or contrast of the result images is still unsatisfied. Instead of using the complicated physical model of underwater imaging degradation, we propose a new method to deal with underwater images by imitating the colour constancy mechanism of human vision using double‐opponency. Firstly, the original image is converted to the LMS space. Then the signals are linearly combined, and Gaussian convolutions are performed to imitate the function of receptive fields (RFs). Next, two RFs with different sizes work together to constitute the double‐opponency response. Finally, the underwater light is estimated to correct the colours in the image. Further contrast stretching on the luminance is optional. Experiments show that the proposed method can obtain clarified underwater images with higher quality than before, and it spends significantly less time cost compared to other previously published typical methods.

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Section: Related Workmentioning

confidence: 99%

Underwater image clarifying based on human visual colour constancy using double‐opponency

Kong

Qian

Song

et al. 2023

CAAI Trans on Intel Tech

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show abstract

“…Tang et al [20] presented an improved DCP algorithm to preprocess underwater monocular vision images. Xie et al [21] used background light estimation and DCP to obtain underwater image restoration. Yu et al [22] proposed a DCP-based underwater image dehazing algorithm by combining homomorphic filtering, double transmission map, and dual-image wavelet fusion.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Underwater Images by CNN-Based Color Balance and Dehazing

Zhu

Luo

2022

Electronics

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Convolutional neural networks (CNNs) are employed to achieve the color balance and dehazing of degraded underwater images. In the module of color balance, an underwater generative adversarial network (UGAN) is constructed. The mapping relationship between underwater images with color deviation and clean underwater images is learned. In the module of clarity improvement, an all-in-one dehazing model is proposed in which a comprehensive index is introduced and estimated by deep CNN. The third module to enhance underwater images adopts an adaptive contrast improvement method by fusing global and local histogram information. Combined with several underwater image datasets, the proposed enhancement method based on the three modules is evaluated, both by subjective visual effects and quantitative evaluation metrics. To demonstrate the advantages of the proposed method, several commonly used underwater image enhancement algorithms are compared. The comparison results indicate that the proposed method gains better enhancement effects for underwater images in different scenes than the other enhancement algorithms, since it can significantly diminish the color deviation, blur, and low contrast in degraded underwater images.

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“…For example, Wang et al [4] introduced an image enhancement algorithm grounded in global airlight estimation and color correction, effectively addressing image degradation and color attenuation phenomena endemic to underwater images. Likewise, Li et al [5] proposed an underwater image enhancement technique hinging on refined dark channel priors and color correction to ameliorate severe color attenuation and the pervasive blue-green color cast witnessed in underwater images. T. Li et al [6] propose an underwater image enhancement framework which comprises an adaptive color restoration module and a haze-line based dehazing module.…”

Section: Introductionmentioning

confidence: 99%

Airlight estimation in underwater image restoration

Chu,

Zhang,

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Underwater imaging is plagued by light absorption and scattering, resulting in distorted, blurry, and low-contrast. This paper introduces an innovative underwater image restoration algorithm that combines natural lighting-based airlight estimation with the refined dark channel prior. The algorithm directly estimates airlight, considering various underwater conditions such as depth, water quality, and camera-object distance, using the Jaffe-McGlamery underwater image formation model tailored for real-world underwater scenarios. A transmission map formula rooted in the refined dark channel prior is then derived. Finally, the algorithm employs the estimated airlight and transmission map to restore the image. Experimental results validate the algorithm's effectiveness in removing airlight artifacts, enhancing image contrast, and providing a clearer and more natural visual output. This approach promises to advance the quality of underwater imaging and its applicability across various domains.

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Underwater Image Restoration Based on Background Light Estimation and Dark Channel Prior

Cited by 8 publications

References 0 publications

Underwater image clarifying based on human visual colour constancy using double‐opponency

Underwater image clarifying based on human visual colour constancy using double‐opponency

Enhancement of Underwater Images by CNN-Based Color Balance and Dehazing

Airlight estimation in underwater image restoration

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