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

2025-12-23

AI Summary

Underwater image degradation—characterized by nonlinear coupling among scattering, color cast, and blur—remains challenging to model, as existing methods typically adopt decoupled designs that fail to capture underlying interdependencies. This paper proposes a unified degradation-handling framework. First, we introduce a novel Joint Feature Mining Module to jointly represent and correct coupled degradations in a holistic manner. Second, we propose a probabilistic self-bootstrapping distribution sampling strategy to enhance robustness against diverse and unpredictable degradation patterns. Third, we design AquaBalanceLoss, the first loss function enabling multi-objective co-optimization of color fidelity, sharpness, and contrast. Implemented on a lightweight network (<1.2M parameters, <15G FLOPs), our method achieves state-of-the-art performance across eight benchmark datasets—including two newly introduced ones—demonstrating superior accuracy, computational efficiency, and cross-dataset generalizability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• Addresses coupled degradations in underwater images
• Proposes JDPNet for unified degradation processing
• Balances color, clarity, and contrast with novel loss function

Innovation

• Joint feature-mining module for coupled degradation processing
• Probabilistic bootstrap distribution strategy for unified adjustment
• AquaBalanceLoss to balance color clarity and contrast