SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising

TL;DR

This paper introduces SSCAN, a novel hyperspectral image denoising network that leverages spectral-spatial attention and group convolutions to improve denoising quality by preserving spectral and spatial details.

Contribution

The paper proposes a new deep learning model, SSCAN, that effectively exploits spectral and spatial correlations in hyperspectral images for denoising, outperforming existing methods.

Findings

Outperforms state-of-the-art HSI denoising algorithms

Uses spectral-spatial attention to enhance feature extraction

Employs residual learning for stable training

Abstract

Hyperspectral images (HSIs) have been widely used in a variety of applications thanks to the rich spectral information they are able to provide. Among all HSI processing tasks, HSI denoising is a crucial step. Recently, deep learning-based image denoising methods have made great progress and achieved great performance. However, existing methods tend to ignore the correlations between adjacent spectral bands, leading to problems such as spectral distortion and blurred edges in denoised results. In this study, we propose a novel HSI denoising network, termed SSCAN, that combines group convolutions and attention modules. Specifically, we use a group convolution with a spatial attention module to facilitate feature extraction by directing models' attention to band-wise important features. We propose a spectral-spatial attention block (SSAB) to exploit the spatial and spectral information in hyperspectral images in an effective manner. In addition, we adopt residual learning operations with skip connections to ensure training stability. The experimental results indicate that the proposed SSCAN outperforms several state-of-the-art HSI denoising algorithms.