Spatial-Spectral Attention-Enhanced Multi-Level Wavelet-Informed Network for Hyperspectral Image Denoising

Hyperspectral image (HSI) stripe noise removal is essential for downstream interpretation tasks. However, most existing methods exhibit incomplete joint modeling of spatial structures and inter-band spectral correlations, lack direction-aware modeling for stripe noise, and lack differentiated processing of high- and low-frequency components. To tackle these limitations, we propose a spatial-spectral attention-enhanced multi-level wavelet-informed network (SAMWNet). Its dual-branch module extracts spatial and spatial-spectral features from each band and its adjacent bands. Afterward, a discrete wavelet-informed progressive denoising (MDWPD) module conducts multi-level Haar wavelet decomposition and progressive reconstruction. Within this module, the low-frequency hybrid enhancement (LFHE) module preserves low-frequency spectral structures, while the high-frequency enhancement (HFME) module suppresses directional stripe artifacts in high-frequency subbands. We further adopt a composite loss function to balance pixel fidelity, noise estimation, structural similarity, and spectral consistency. Experimental results on simulated and real-world HSIs demonstrate that SAMWNet achieves competitive or superior performance compared with several representative HSI denoising methods.