Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba

• URL: http://arxiv.org/abs/2512.03852v1
• Date: Wed, 03 Dec 2025 14:50:20 GMT
• Title: Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba
• Authors: Liwen Pan, Longguang Wang, Guangwei Gao, Jun Wang, Jun Shi, Juncheng Li,
• Abstract summary: We propose Frequency-Aware Mamba (FAMamba), a novel framework that integrates frequency guidance with sequence modeling for efficient image restoration.<n>Our architecture consists of two key components: (1) a Dual-Branch Feature Extraction Block (DFEB) that enhances local-global interaction via bidirectional 2D frequency-adaptive scanning, and (2) a Prior-Guided Block (PGB) that refines texture details through wavelet-based high-frequency residual learning.
• Score: 37.901352525347214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic image restoration under adverse weather conditions remains a critical challenge for intelligent transportation systems. Existing methods primarily focus on spatial-domain modeling but neglect frequency-domain priors. Although the emerging Mamba architecture excels at long-range dependency modeling through patch-wise correlation analysis, its potential for frequency-domain feature extraction remains unexplored. To address this, we propose Frequency-Aware Mamba (FAMamba), a novel framework that integrates frequency guidance with sequence modeling for efficient image restoration. Our architecture consists of two key components: (1) a Dual-Branch Feature Extraction Block (DFEB) that enhances local-global interaction via bidirectional 2D frequency-adaptive scanning, dynamically adjusting traversal paths based on sub-band texture distributions; and (2) a Prior-Guided Block (PGB) that refines texture details through wavelet-based high-frequency residual learning, enabling high-quality image reconstruction with precise details. Meanwhile, we design a novel Adaptive Frequency Scanning Mechanism (AFSM) for the Mamba architecture, which enables the Mamba to achieve frequency-domain scanning across distinct subgraphs, thereby fully leveraging the texture distribution characteristics inherent in subgraph structures. Extensive experiments demonstrate the efficiency and effectiveness of FAMamba.

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