
The proposed Spatial-Spectral U-Mamba (SSUMamba) model leverages the linear complexity of the Selective State Space Model (SSM) to effectively capture the global spatial-spectral correlation in hyperspectral images, enabling efficient and high-quality denoising.


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spatial-spectral-selective-state-space-model-for-efficient-hyperspectral-image-denoising


Spatial-Spectral Selective State Space Model for Efficient Hyperspectral Image Denoising


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The proposed HSDM model effectively captures spatial-spectral dependencies in hyperspectral images using a novel bidirectional continuous scanning mechanism within a selective state space model framework, achieving state-of-the-art denoising performance with high computational efficiency.


efficient-hyperspectral-image-denoising-with-bidirectional-state-space-models


Efficient Hyperspectral Image Denoising with Bidirectional State-Space Models
