Underwater Imaging Formation Model-Embedded Multiscale Deep Neural Network for Underwater Image Enhancement

Fucui Li, Difei Lu, ChengLang Lu, Qiuping Jiang and Abílio De Jesus

Mathematical Problems in Engineering, 2022, vol. 2022, 1-11

Abstract: Underwater image enhancement (UIE) plays an essential role in improving the quality of raw images captured in an underwater environment. Existing UIE methods can be categorized into two types: handcraft-designed and deep learning-based methods. Generally, the handcraft-designed methods are more explainable due to the leverage of knowledge-based image priors, while the deep learning-based methods are usually criticized for their weak interpretability. In this study, we address this issue by integrating the merits of both handcraft-designed and deep learning-based methods. Specifically, a physical underwater imaging model-inspired deep CNN for UIE is designed. Instead of estimating a global background light magnitude and a transmission matrix separately in traditional image restoration-based UIE methods, we directly generate a single variable as the joint estimation of these two parameters within a deep CNN and directly recover the enhanced image as an output according to a reformulated physical underwater imaging model. The whole network is trained in an end-to-end manner and more importantly has good interpretability. The proposed method has been validated for the UIE task on a real-world underwater image dataset and the experimental results well demonstrate the superiority of our method over the existing ones for UIE.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8330985

DOI: 10.1155/2022/8330985

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