  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.
