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Underwater Image Quality: Enhancement and Evaluation

机译:水下图像质量:增强和评估

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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.
机译:水下光学图像通常由相机捕获,但具有不同的统计特征到自然图像。由于不同水类型中的光的折射和散射,可以扭曲物体的颜色和形状,其无法提供可接受的视觉质量。因此,它必须开发算法以增强水下图像。此外,水下图像的质量评估也被利用作为水下图像增强的标准。在过去十年中,相关问题引起了相当大的关注。本文提出了对相关技术的全面审查及其最近的成就。特别是,我们在小体积的数据中观察到在水下图像处理中应用深度学习的重要趋势。我们希望我们的审核可以使初学者和该领域的专家受益,以发现吸引人和具有挑战性的研究主题。

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