SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising

arXiv:2405.01726v1 Announce Type: cross
Abstract: Denoising hyperspectral images (HSIs) is a crucial preprocessing procedure due to the noise originating from intra-imaging mechanisms and environmental factors. Utilizing domain-specific knowledge of HSIs, such as spectral correlation, spatial self-similarity, and spatial-spectral correlation, is essential for deep learning-based denoising. Existing methods are often constrained by running time, space complexity, and computational complexity, employing strategies that explore these priors separately. While the strategies can avoid some redundant information, considering that hyperspectral images are 3-D images …

arxiv cs.cv cs.lg denoising eess.iv image space spatial state state space model type