RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing

TL;DR

RSDehamba is a lightweight, efficient neural network that combines SSM and U-Net architecture to improve remote sensing image dehazing by capturing global context and spatial variations effectively.

Contribution

It introduces the first lightweight mamba-based model for RSID, integrating SSM into U-Net with novel VDB and DSM modules for enhanced feature extraction.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Demonstrates superior global context encoding and spatial feature extraction

Achieves high-quality dehazing with reduced computational complexity

Abstract

Remote sensing image dehazing (RSID) aims to remove nonuniform and physically irregular haze factors for high-quality image restoration. The emergence of CNNs and Transformers has taken extraordinary strides in the RSID arena. However, these methods often struggle to demonstrate the balance of adequate long-range dependency modeling and maintaining computational efficiency. To this end, we propose the first lightweight network on the mamba-based model called RSDhamba in the field of RSID. Greatly inspired by the recent rise of Selective State Space Model (SSM) for its superior performance in modeling linear complexity and remote dependencies, our designed RSDehamba integrates the SSM framework into the U-Net architecture. Specifically, we propose the Vision Dehamba Block (VDB) as the core component of the overall network, which utilizes the linear complexity of SSM to achieve the capability of global context encoding. Simultaneously, the Direction-aware Scan Module (DSM) is designed to dynamically aggregate feature exchanges over different directional domains to effectively enhance the flexibility of sensing the spatially varying distribution of haze. In this way, our RSDhamba fully demonstrates the superiority of spatial distance capture dependencies and channel information exchange for better extraction of haze features. Extensive experimental results on widely used benchmarks validate the surpassing performance of our RSDehamba against existing state-of-the-art methods.