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

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.