HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising

arXiv:2404.09697v1 Announce Type: new
Abstract: Effectively discerning spatial-spectral dependencies in HSI denoising is crucial, but prevailing methods using convolution or transformers still face computational efficiency limitations. Recently, the emerging Selective State Space Model(Mamba) has risen with its nearly linear computational complexity in processing natural language sequences, which inspired us to explore its potential in handling long spectral sequences. In this paper, we propose HSIDMamba(HSDM), tailored to exploit the linear complexity for effectively capturing spatial-spectral dependencies in HSI denoising. In particular, …

abstract arxiv complexity computational convolution cs.cv denoising dependencies efficiency explore face language limitations linear mamba natural natural language processing space spatial state state space model transformers type