June 11, 2024, 4:50 a.m. | Hang Fu, Genyun Sun, Yinhe Li, Jinchang Ren, Aizhu Zhang, Cheng Jing, Pedram Ghamisi

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.05700v1 Announce Type: new
Abstract: Haze contamination in hyperspectral remote sensing images (HSI) can lead to spatial visibility degradation and spectral distortion. Haze in HSI exhibits spatial irregularity and inhomogeneous spectral distribution, with few dehazing networks available. Current CNN and Transformer-based dehazing methods fail to balance global scene recovery, local detail retention, and computational efficiency. Inspired by the ability of Mamba to model long-range dependencies with linear complexity, we explore its potential for HSI dehazing and propose the first HSI …

arxiv cs.cv eess.iv sensing space state state space model type

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