Local–Global Feature-Aware Transformer Based Residual Network for Hyperspectral Image Denoising

Hyperspectral images (HSIs) are generally distorted by various types of damage and degradation due to limited imaging conditions. Hence, noise reduction is an essential process before HSI interpretations and applications. In this paper, a novel local-global feature-aware transformer based residual network (FATR) is proposed for hyperspectral image denoising. First, a spatial-spectral feature extraction module is built to extract spatial and spectral shallow features simultaneously. Second, these spatial-spectral features are forwarded to the deep feature extraction module, which contains several local-global feature-aware transformer blocks, where contextual information as well as local and global information can be further aggregated by multiscale windows transformer layers. Finally, in the reconstruction module, different hierarchical features from branches of two modules are merged into the final restoration to recover clean HSIs. Extensive experiments on both synthetic and real-world data demonstrate that the model has a better ability to restore HSIs in terms of evaluation metrics and visual assessments.