SARM: Scene-Aware Retinex Mamba for Underwater Image Enhancement.
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| Title: | SARM: Scene-Aware Retinex Mamba for Underwater Image Enhancement. |
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| Authors: | Fu, Zhanbo1 (AUTHOR), Yang, Shuang2 (AUTHOR), Sun, Aiguo2,3 (AUTHOR), Xiong, Rongjun2,4 (AUTHOR), Chen, Nengcheng1,2 (AUTHOR) chennengcheng@cug.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1652. 30p. |
| Subjects: | Image enhancement (Imaging systems), State-space methods, Underwater imaging systems, Underwater photography, Image quality analysis, Machine learning |
| Abstract: | Highlights: What are the main findings? SARM achieves the deep integration of Retinex physical priors and state space models (SSMs), providing a new perspective for self-supervised underwater image enhancement. Scene-aware adaptation and global linear complexity modeling techniques yield significant image quality improvements across multiple underwater visual benchmarks. What are the implications of the main findings? The prior-guided mechanism provides an effective paradigm for tackling paired data scarcity and highly heterogeneous degradations in real waters, highlighting the importance of physical laws in deep feature modeling. The framework proposes an efficient and highly generalizable enhancement strategy that can serve as a visual preprocessing front-end for marine edge devices, enabling stable performance gains in downstream underwater tasks such as feature matching and edge extraction. Underwater image enhancement is essential for marine visual perception tasks. However, the highly heterogeneous optical degradations in real-world waters, the scarcity of paired training data, and the inherent dilemma for existing models in balancing long-range dependency modeling with computational overhead pose significant challenges. To address these issues, this paper proposes a prior-guided, self-supervised underwater image enhancement framework called Scene-Aware Retinex Mamba (SARM). This framework seamlessly integrates Retinex theoretical priors with state space models (SSMs) and operates without paired supervision by employing a prior-guided pseudo-labeling strategy to guide network optimization. Architecturally, SARM deeply couples the physical Retinex prior with SSM. Its core module integrates multi-color space features and leverages a 2D selective scan mechanism to achieve global context modeling with linear complexity O (H W) , effectively removing complex color casts and suppressing non-uniform scattering noise. To further overcome the generalization bottlenecks in cross-domain underwater testing, this paper introduces a Scene-Aware Adapter (SAA), which facilitates dynamic loss scheduling and adaptive feature gating by quantifying scene-specific degradation characteristics. Comprehensive evaluations on multiple benchmark datasets, including UIEB, EUVP, and UCCS, demonstrate that SARM achieves state-of-the-art subjective and objective enhancement quality (e.g., yielding a URanker score of 2.491 and a CCF score of 35.76), while maintaining an ultra-fast inference speed of 136.52 FPS on the UIEB dataset. Furthermore, extended experiments reveal that SARM can significantly boost the performance of downstream vision tasks, validating its potential as a robust preprocessing module for various practical marine vision applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? SARM achieves the deep integration of Retinex physical priors and state space models (SSMs), providing a new perspective for self-supervised underwater image enhancement. Scene-aware adaptation and global linear complexity modeling techniques yield significant image quality improvements across multiple underwater visual benchmarks. What are the implications of the main findings? The prior-guided mechanism provides an effective paradigm for tackling paired data scarcity and highly heterogeneous degradations in real waters, highlighting the importance of physical laws in deep feature modeling. The framework proposes an efficient and highly generalizable enhancement strategy that can serve as a visual preprocessing front-end for marine edge devices, enabling stable performance gains in downstream underwater tasks such as feature matching and edge extraction. Underwater image enhancement is essential for marine visual perception tasks. However, the highly heterogeneous optical degradations in real-world waters, the scarcity of paired training data, and the inherent dilemma for existing models in balancing long-range dependency modeling with computational overhead pose significant challenges. To address these issues, this paper proposes a prior-guided, self-supervised underwater image enhancement framework called Scene-Aware Retinex Mamba (SARM). This framework seamlessly integrates Retinex theoretical priors with state space models (SSMs) and operates without paired supervision by employing a prior-guided pseudo-labeling strategy to guide network optimization. Architecturally, SARM deeply couples the physical Retinex prior with SSM. Its core module integrates multi-color space features and leverages a 2D selective scan mechanism to achieve global context modeling with linear complexity O (H W) , effectively removing complex color casts and suppressing non-uniform scattering noise. To further overcome the generalization bottlenecks in cross-domain underwater testing, this paper introduces a Scene-Aware Adapter (SAA), which facilitates dynamic loss scheduling and adaptive feature gating by quantifying scene-specific degradation characteristics. Comprehensive evaluations on multiple benchmark datasets, including UIEB, EUVP, and UCCS, demonstrate that SARM achieves state-of-the-art subjective and objective enhancement quality (e.g., yielding a URanker score of 2.491 and a CCF score of 35.76), while maintaining an ultra-fast inference speed of 136.52 FPS on the UIEB dataset. Furthermore, extended experiments reveal that SARM can significantly boost the performance of downstream vision tasks, validating its potential as a robust preprocessing module for various practical marine vision applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101652 |
