JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement

TL;DR

JDPNet is a novel neural network designed to effectively process and enhance underwater images by unifying the handling of complex, coupled degradations through joint feature mining and a specialized loss function.

Contribution

The paper introduces JDPNet, which uniquely mines and unifies coupled degradation information in underwater images, improving enhancement performance over existing methods.

Findings

Achieves state-of-the-art results on six public datasets.

Balances enhancement quality with computational efficiency.

Effectively handles complex degradation interactions.

Abstract

Given the complexity of underwater environments and the variability of water as a medium, underwater images are inevitably subject to various types of degradation. The degradations present nonlinear coupling rather than simple superposition, which renders the effective processing of such coupled degradations particularly challenging. Most existing methods focus on designing specific branches, modules, or strategies for specific degradations, with little attention paid to the potential information embedded in their coupling. Consequently, they struggle to effectively capture and process the nonlinear interactions of multiple degradations from a bottom-up perspective. To address this issue, we propose JDPNet, a joint degradation processing network, that mines and unifies the potential information inherent in coupled degradations within a unified framework. Specifically, we introduce a joint feature-mining module, along with a probabilistic bootstrap distribution strategy, to facilitate effective mining and unified adjustment of coupled degradation features. Furthermore, to balance color, clarity, and contrast, we design a novel AquaBalanceLoss to guide the network in learning from multiple coupled degradation losses. Experiments on six publicly available underwater datasets, as well as two new datasets constructed in this study, show that JDPNet exhibits state-of-the-art performance while offering a better tradeoff between performance, parameter size, and computational cost.