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

TL;DR

WDMamba introduces a wavelet-based, two-stage dehazing framework that effectively restores images by separating low-frequency haze removal from detail enhancement, utilizing a novel regularization technique for improved performance.

Contribution

The paper proposes a novel wavelet degradation prior and a coarse-to-fine dehazing framework with a self-guided contrastive regularization, advancing state-of-the-art image dehazing methods.

Findings

Outperforms existing methods on public benchmarks

Effectively captures haze-specific features in low-frequency components

Enhances detail retention with contrastive regularization

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.