Underwater Image Quality: Enhancement and Evaluation

Nadir Mustafa A. Mohamed, Liqun Lin, Weiling Chen, Hongan Wei
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引用次数: 4

Abstract

The underwater optical images are commonly captured by camera but with different statistical features to natural images. Due to the refraction and scattering of light in different water types, the colors and shapes of objects can be twisted that are incapable of providing acceptable visual qualities. Thus, it is imperative to develop algorithms to enhance underwater images. Besides, the quality evaluation of underwater images is also exploited as a criteria of underwater image enhancement. In the past decade, the related issues have attracted considerable attention. This paper presents a comprehensive review of the related techniques and their most recent achievements. In particular, we observe a significant trend of applying deep learning in underwater image processing in a small volume of data. We hope our review could benefit both the beginners and the experts of this area for discovering appealing and challenging research topics.
水下图像质量:增强和评价
水下光学图像通常是由相机捕获的,但与自然图像具有不同的统计特征。由于光线在不同类型的水中的折射和散射,物体的颜色和形状会被扭曲,无法提供可接受的视觉质量。因此,开发增强水下图像的算法势在必行。此外,还将水下图像的质量评价作为水下图像增强的标准。在过去的十年中,相关问题引起了相当大的关注。本文对相关技术及其最新成果进行了综述。特别是,我们观察到在小数据量的水下图像处理中应用深度学习的显著趋势。我们希望我们的综述能够帮助这一领域的初学者和专家发现有吸引力和具有挑战性的研究课题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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