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

• URL: http://arxiv.org/abs/2512.20213v1
• Date: Tue, 23 Dec 2025 10:12:35 GMT
• Title: JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement
• Abstract summary: JDPNet is a joint degradation processing network.<n>It unifies the potential information inherent in degradation coupleds within a unified framework.<n>It exhibits state-of-the-art performance while offering a better tradeoff between performance, parameter size, and computational cost.
• Score: 9.989461961314753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Variational Deep Unfolding with Mamba-Based Nonlocal Modeling for Underwater Image Enhancement [1.3066182802188202]
  We propose a model-based deep unfolding network for underwater image enhancement.<n>The framework is guided by a variational formulation based on a dehazing decomposition.<n>The proposed unfolding method incorporates Mamba layers to efficiently capture self-similarities in the scene.
  arXiv  Detail & Related papers  (2026-06-10T09:42:30Z)
• Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement [22.353926184394002]
  Single-scale feature extraction can match or surpass the performance of multi-scale methods.<n>SSD-Net combines CNN's local feature extraction capabilities with Transformer's global modeling strengths.
  arXiv  Detail & Related papers  (2025-08-06T06:41:58Z)
• DACA-Net: A Degradation-Aware Conditional Diffusion Network for Underwater Image Enhancement [16.719513778795367]
  Underwater images typically suffer from severe colour distortions, low visibility, and reduced structural clarity due to complex optical effects such as scattering and absorption.<n>Existing enhancement methods often struggle to adaptively handle diverse degradation conditions and fail to leverage underwater-specific physical priors effectively.<n>We propose a degradation-aware conditional diffusion model to enhance underwater images adaptively and robustly.
  arXiv  Detail & Related papers  (2025-07-30T09:16:07Z)
• UniUIR: Considering Underwater Image Restoration as An All-in-One Learner [62.65503609562905]
  We propose a Universal Underwater Image Restoration method, termed as UniUIR.<n>To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module.<n>This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts.
  arXiv  Detail & Related papers  (2025-01-22T16:10:42Z)
• Blind Underwater Image Restoration using Co-Operational Regressor Networks [15.853520058218042]
  We propose a novel machine learning model, Co-Operational Regressor Networks (CoRe-Nets)<n>A CoRe-Net consists of two co-operating networks: the Apprentice Regressor (AR), responsible for image transformation, and the Master Regressor (MR), which evaluates the Peak Signal-to-Noise Ratio (PSNR) of the images generated by the AR and feeds it back to AR.<n>Our results and the optimized PyTorch implementation of the proposed approach are now publicly shared on GitHub.
  arXiv  Detail & Related papers  (2024-12-05T09:15:21Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators [120.06891448820447]
  How to obtain clear and visually pleasant images has become a common concern of people. The task of underwater image enhancement (UIE) has also emerged as the times require. In this paper, we propose a physical model-guided GAN model for UIE, referred to as PUGAN. Our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics.
  arXiv  Detail & Related papers  (2023-06-15T07:41:12Z)
• Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network [101.53907377000445]
  Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. Existing methods result in the loss of middle-layer features due to activation functions. We propose a Feature Interaction Weighted Hybrid Network (FIWHN) to minimize the impact of intermediate feature loss on reconstruction quality.
  arXiv  Detail & Related papers  (2022-12-29T05:57:29Z)
• Domain Adaptation for Underwater Image Enhancement [51.71570701102219]
  We propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to minimize the inter-domain and intra-domain gap. In the first phase, a new dual-alignment network is designed, including a translation part for enhancing realism of input images, followed by an enhancement part. In the second phase, we perform an easy-hard classification of real data according to the assessed quality of enhanced images, where a rank-based underwater quality assessment method is embedded.
  arXiv  Detail & Related papers  (2021-08-22T06:38:19Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.