VISIONARY: Novel Spatial-Spectral Attention Mechanism for Hyperspectral Image Denoising

Image denoising mitigates noise from the captured images and thereby, enhances the efficacy of high-demand vision applications, such as classification and segmentation. Hyperspectral Images (HSIs), with their multiple spectral bands, provide valuable information and make them highly applicable in real-world applications. Current Deep Learning methods mainly employ Transformers to denoise HSIs spatially and spectrally through self-attention (SA). However, SA focuses on individual samples and overlooks potential correlations within the images, indicating room for improvement. Moreover, existing Transformer-based denoising methods often fail to appropriately balance the importance of spatial and spectral features. This paper presents a novel method, VISIONARY, to address these issues by obtaining better HSI feature representation. To this end, it introduces the Spatial-Spectral-Cubic Transformer (SS-Cformer) block to address the shortcomings of Transformers in HSI denoising, particularly their inability to capture correlations within images of the same type, by introducing Global Feature Attention (GFA). Additionally, the SSC-former independently determines the optimal weightage for spatial and spectral features using attention mechanisms, leading to more effective denoising. Our method, VISIONARY is based on the integration of Transformer, U-Net and CNN architecture. Experimental results demonstrate that VISIONARY outperforms well-known methods on publicly available datasets, and our SSCformer block can be easily integrated with existing Transformer-based HSI denoising methods to improve their efficacy.