DACA-Net: A Degradation-Aware Conditional Diffusion Network for Underwater Image Enhancement

TL;DR

DACA-Net is a novel degradation-aware conditional diffusion model that adaptively enhances underwater images by predicting degradation levels and leveraging underwater-specific priors, resulting in superior visual restoration.

Contribution

Introduces a degradation-aware conditional diffusion network with physical priors and adaptive noise scheduling for robust underwater image enhancement.

Findings

Outperforms state-of-the-art methods in color fidelity and structural detail restoration.

Effectively predicts degradation levels for adaptive enhancement.

Achieves significant improvements in quantitative metrics and visual quality.

Abstract

Underwater images typically suffer from severe colour distortions, low visibility, and reduced structural clarity due to complex optical effects such as scattering and absorption, which greatly degrade their visual quality and limit the performance of downstream visual perception tasks. Existing enhancement methods often struggle to adaptively handle diverse degradation conditions and fail to leverage underwater-specific physical priors effectively. In this paper, we propose a degradation-aware conditional diffusion model to enhance underwater images adaptively and robustly. Given a degraded underwater image as input, we first predict its degradation level using a lightweight dual-stream convolutional network, generating a continuous degradation score as semantic guidance. Based on this score, we introduce a novel conditional diffusion-based restoration network with a Swin UNet backbone, enabling adaptive noise scheduling and hierarchical feature refinement. To incorporate underwater-specific physical priors, we further propose a degradation-guided adaptive feature fusion module and a hybrid loss function that combines perceptual consistency, histogram matching, and feature-level contrast. Comprehensive experiments on benchmark datasets demonstrate that our method effectively restores underwater images with superior colour fidelity, perceptual quality, and structural details. Compared with SOTA approaches, our framework achieves significant improvements in both quantitative metrics and qualitative visual assessments.