WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing

• URL: http://arxiv.org/abs/2505.04369v1
• Date: Wed, 07 May 2025 12:37:01 GMT
• Title: WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing
• Authors: Jie Sun, Heng Liu, Yongzhen Wang, Xiao-Ping Zhang, Mingqiang Wei,
• Abstract summary: We propose a novel dehazing framework, WDMamba, which decomposes the image dehazing task into two sequential stages.<n>In the low-frequency restoration stage, we integrate Mamba blocks to reconstruct global structures with linear complexity, efficiently removing overall haze.<n>In the detail enhancement stage, we reinstate fine-grained information that may have been overlooked during the previous stage, culminating in the final dehazed output.
• Score: 28.199334898731646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we reveal a novel haze-specific wavelet degradation prior observed through wavelet transform analysis, which shows that haze-related information predominantly resides in low-frequency components. Exploiting this insight, we propose a novel dehazing framework, WDMamba, which decomposes the image dehazing task into two sequential stages: low-frequency restoration followed by detail enhancement. This coarse-to-fine strategy enables WDMamba to effectively capture features specific to each stage of the dehazing process, resulting in high-quality restored images. Specifically, in the low-frequency restoration stage, we integrate Mamba blocks to reconstruct global structures with linear complexity, efficiently removing overall haze and producing a coarse restored image. Thereafter, the detail enhancement stage reinstates fine-grained information that may have been overlooked during the previous phase, culminating in the final dehazed output. Furthermore, to enhance detail retention and achieve more natural dehazing, we introduce a self-guided contrastive regularization during network training. By utilizing the coarse restored output as a hard negative example, our model learns more discriminative representations, substantially boosting the overall dehazing performance. Extensive evaluations on public dehazing benchmarks demonstrate that our method surpasses state-of-the-art approaches both qualitatively and quantitatively. Code is available at https://github.com/SunJ000/WDMamba.

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