Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement

• URL: http://arxiv.org/abs/2508.04123v1
• Date: Wed, 06 Aug 2025 06:41:58 GMT
• Title: Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement
• Authors: Zheng Cheng, Wenri Wang, Guangyong Chen, Yakun Ju, Yihua Cheng, Zhisong Liu, Yanda Meng, Jintao Song,
• Abstract summary: 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.
• Score: 22.353926184394002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater image enhancement (UIE) techniques aim to improve visual quality of images captured in aquatic environments by addressing degradation issues caused by light absorption and scattering effects, including color distortion, blurring, and low contrast. Current mainstream solutions predominantly employ multi-scale feature extraction (MSFE) mechanisms to enhance reconstruction quality through multi-resolution feature fusion. However, our extensive experiments demonstrate that high-quality image reconstruction does not necessarily rely on multi-scale feature fusion. Contrary to popular belief, our experiments show that single-scale feature extraction alone can match or surpass the performance of multi-scale methods, significantly reducing complexity. To comprehensively explore single-scale feature potential in underwater enhancement, we propose an innovative Single-Scale Decomposition Network (SSD-Net). This architecture introduces an asymmetrical decomposition mechanism that disentangles input image into clean layer along with degradation layer. The former contains scene-intrinsic information and the latter encodes medium-induced interference. It uniquely combines CNN's local feature extraction capabilities with Transformer's global modeling strengths through two core modules: 1) Parallel Feature Decomposition Block (PFDB), implementing dual-branch feature space decoupling via efficient attention operations and adaptive sparse transformer; 2) Bidirectional Feature Communication Block (BFCB), enabling cross-layer residual interactions for complementary feature mining and fusion. This synergistic design preserves feature decomposition independence while establishing dynamic cross-layer information pathways, effectively enhancing degradation decoupling capacity.

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