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A systematic review of the methodologies for the processing and enhancement of the underwater images

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Abstract

Underwater image processing has received tremendous attention in the past few years. The reason for increased research in this area is that the process of taking images underwater is very difficult. Images obtained underwater frequently suffer from quality deterioration issues such as poor contrast, blurring features, colour variations, non-uniform lighting, the presence of dust particles, noise at the bottom of the sea, different properties of the water medium, and so on. The improvement of underwater images is a critical problem in image processing and computer vision for a variety of practical applications. To address this problem, we need to find some other methods to increase the quality of the image while capturing it underwater. But capturing the image in normal circumstances as well as underwater is the same, so once we get an image, some mechanism to increase the quality of the captured image will also be required. A complete and in-depth study of relevant accomplishments and developments, particularly the survey of underwater image methods and datasets, which are a critical issue in underwater image processing and intelligent application, is still lacking. In this paper, we first provide a review of more than 85 articles on the most recent advancements in underwater image restoration methods, underwater image enhancement methods, and underwater image enhancement using deep learning and machine learning methods, along with the techniques, data sets, and evaluation criteria. To provide a thorough grasp of underwater image restoration, enhancement, and enhancement using deep learning and machine learning, we explore the strengths and limits of existing techniques. Additionally, we offer thorough, unbiased reviews and evaluations of the representative methodologies for five distinct types of underwater situations, which vary their usefulness in various underwater circumstances. Two main evaluations, subjective image quality evaluation and objective image quality evaluation; are used for evaluating the quality of images. These evaluations are useful to determine the efficiency of the predefined methods. With the help of these image quality evaluations, we come to the conclusion that the image enhancement methods and image enhancement methods using deep learning and machine learning are superior in comparison to the image restoration methods. As deep learning and machine learning based enhancement methods are newer and give far better results in comparison to the other two methods, lots of researchers are moving towards these methods. Finally, we also explore the potential difficulties and unresolved problems associated with underwater image enhancement and offer potential future research areas.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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  1. Department of Computer Science & Engineering, Delhi Technological University, Delhi, Delhi, 110042, India

    Nishant Singh & Aruna Bhat

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  1. Nishant Singh
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Singh, N., Bhat, A. A systematic review of the methodologies for the processing and enhancement of the underwater images. Multimed Tools Appl 82, 38371–38396 (2023). https://doi.org/10.1007/s11042-023-15156-9

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