---
title: 'DeRainMamba: Frequency-Aware Image Deraining'
url: https://www.emergentmind.com/topics/derainmamba
type: topic
---

# DeRainMamba: Frequency-Aware Image Deraining

DeRainMamba is a frequency-aware state space model designed for single image deraining. By integrating frequency-domain discrimination with multi-directional spatial enhancement within a unified U-Net architecture, DeRainMamba addresses the shortcomings of prior CNN and SSM models, yielding substantial improvements in both rain streak removal and detail preservation. The framework is characterized by its Frequency-Aware State-Space Module (FASSM) and Multi-Directional Perception Convolution (MDPConv), enabling accurate rain detection in the frequency domain and robust local structure recovery in the spatial domain. Extensive empirical studies indicate that DeRainMamba outperforms existing state-of-the-art methods in terms of both restoration quality and computational efficiency [2510.06746].

## 1. Motivation and Theoretical Foundation

DeRainMamba was developed to reconcile the global context modeling strengths of long-sequence models with the sensitivity to fine local details. Conventional CNN-based derainers possess limited receptive fields, impeding global artifact removal. Transformer-based frameworks improve context modeling but incur high computational cost for large images. Prior state space models in the Mamba family achieve efficient sequence modeling but lack frequency-domain awareness, hampering discrimination between rain streaks and natural image details. Rain artifacts manifest predominantly in mid- and high-frequency bands; thus, effective deraining requires both global frequency analysis and fine spatial enhancement.

## 2. Frequency-Aware State-Space Module (FASSM)

FASSM is central to DeRainMamba’s ability to discern rain streaks without sacrificing image detail:

- The input feature $F_{in}$ is first normalized using Layer Normalization (LN).
- Two parallel branches operate:
    - Vision State Space Module (VSSM): Provides global spatial modeling within the state-space framework.
    - Residual Fourier Module (RFM): Projects $F_{in}$ to the frequency domain via FFT, processes amplitude $\mathcal{A}(X)$ and phase $\mathcal{P}(X)$ separately, then reconstructs the feature via inverse FFT and $1\times1$ convolution.
- The final output combines three terms:
    $$
    F_{out} = \text{VSSM}(\text{LN}(F_{in})) + \text{RFM}(F_{in}) + s \cdot F_{in}
    $$
    where $s$ is a learnable blending scalar.

- Fourier processing specifically:
    - Amplitude and phase are extracted:
        $$
        \mathcal{A}(X) = |\mathcal{V}(F(X))|, \qquad \mathcal{P}(X) = \angle \mathcal{V}(F(X))
        $$
    - Enhanced representation:
        $$
        \hat{H}(X) = \text{ReLU}(\text{Conv}_{1\times1}(\mathcal{A}(X)) + \text{Conv}_{1\times1}(\mathcal{P}(X)))
        $$
        $$
        X_{out} = \text{Conv}_{1\times1}(\mathcal{V}^{-1}(\hat{H}(X))) + \text{DWConv}(X)
        $$
      where $\text{DWConv}$ is a $3\times3$ depthwise convolution.
      
This strategy efficiently highlights the spectral signatures of rain, enabling aggressive suppression without loss of texture.

## 3. Multi-Directional Perception Convolution (MDPConv)

MDPConv restores local structure by explicit gradient-based feature extraction from multiple orientations:

- Five differential convolution branches (horizontal, vertical, angular, central difference, and vanilla convolution) derive directional gradient maps, facilitating the isolation of rain streaks from edges and contours.
- For scalability and inference efficiency, a re-parameterization merges these kernels so that at deployment only a single “equivalent kernel” ($K_{eq}$) is needed:
    $$
    F_{out} = \text{MDPConv}(F_{in}) = \sum_{i=1}^5 F_{in} \ast K_i = F_{in} \ast (\sum_{i=1}^5 K_i) = F_{in} \ast K_{eq}
    $$
This design maintains directional sensitivity while minimizing inference cost, lending superior edge preservation and artifact rejection.

## 4. Experimental Performance and Benchmarks

DeRainMamba was extensively validated on Rain200L, Rain200H, DID-Data, and DDN-Data using PSNR and SSIM:

| Benchmark   | DeRainMamba PSNR | SOTA Comparator | ΔPSNR | DeRainMamba SSIM | SOTA Comparator | ΔSSIM   |
|-------------|------------------|-----------------|-------|------------------|-----------------|---------|
| Rain200H    | 36.71            | DRSformer: 36.26| +0.45 | 0.9833           | DRSformer:0.9765| +0.0068 |
| DID-Data    | 32.84            | MambaIR: 32.61  | +0.23 | 0.9641           | MambaIR:0.9607  | +0.0034 |

Quantitative improvement over previous models is accompanied by qualitative evidence: DeRainMamba consistently removed pronounced rain streaks and preserved fine texture and natural color fidelity.

## 5. Computational Efficiency

DeRainMamba achieves high performance with only 27.8 million parameters—substantially lower than models such as Uformer ($\sim$50.9M) and MambaIR ($\sim$31.5M):

- Parameter reduction arises from the integration of efficient state-space modeling and re-parameterized MDPConv.
- Inference acceleration is obtained by fusing multi-branch convolutions into a single kernel, eliminating computational redundancy.
- This enables deployment in resource-constrained or real-time scenarios, extending practical utility beyond research-grade settings.

## 6. Practical Implications and Future Directions

The presented frequency-aware, detail-enhancing architecture demonstrates that simultaneous modeling of global spectral context and local spatial details yields superior deraining, especially visible under challenging benchmark conditions. While DeRainMamba raises state-of-the-art performance under standard conditions, unresolved challenges remain for extreme rain scenarios and complex degradations. Future work suggested includes generalization improvements—potentially via more advanced frequency-spatial fusions—and expanding robustness to more diverse environmental corruptions.

## 7. Context and Significance

DeRainMamba exemplifies the trend of hybrid state-space and frequency-domain modeling in low-level vision restoration. The balancing of Fourier-based discrimination with anisotropic spatial enhancement provides a unified framework that addresses the limitations of pure CNN or transformer/SSM architectures [2510.06746]. Its design underscores the value of detail-preserving priors and adaptive frequency-aware processing in achieving high-quality restoration within efficient, scalable networks.

Source: https://www.emergentmind.com/topics/derainmamba