RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing
Huiling Zhou, Xianhao Wu, Hongming Chen, Xiang Chen, Xin He

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Enhancement Techniques
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Concatenated Skip Connection · Max Pooling · Convolution · U-Net
