Abstract
In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift, low contrast, noise, and blurring issues. This paper aims to classify and compare some of the significant technologies in underwater image defogging, presenting a comprehensive picture of the current research landscape for researchers. First we analyze the reasons for degradation of underwater images and the underwater optical imaging model. Then we classify the underwater image defogging technologies into three categories, including image restoration approaches, image enhancement approaches, and deep learning approaches. Afterward, we present the objective evaluation metrics and analyze the state-of-the-art approaches. Finally, we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.
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Jing-chun ZHOU conceived the idea and drafted the paper. De-huan ZHANG carried out the experiments. Wei-shi ZHANG revised the paper and provided technical guidance. Jing-chun ZHOU and De-huan ZHANG revised and finalized the paper.
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Jing-chun ZHOU, De-huan ZHANG, and Wei-shi ZHANG declare that they have no conflict of interest.
Project supported by the National Natural Science Foundation of China (No. 61702074), the Liaoning Provincial Natural Science Foundation of China (No. 20170520196), and the Fundamental Research Funds for the Central Universities, China (Nos. 3132019205 and 3132019354)
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Zhou, Jc., Zhang, Dh. & Zhang, Ws. Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey. Front Inform Technol Electron Eng 21, 1745–1769 (2020). https://doi.org/10.1631/FITEE.2000190
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DOI: https://doi.org/10.1631/FITEE.2000190