WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing
Journal:
arXiv
Published Date:
May 7, 2025
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.
