Hyperspectral image denoising via self-modulated cross-attention deformable convolutional neural network

Abstract

Compared with ordinary images, hyperspectral images (HSIs) consist of many bands that can provide rich spatial and spectral information and are widely used in remote sensing. However, HSIs are subject to various types of noise due to limited sensor sensitivity; low light intensity in the bands; and corruption during acquisition, transmission, and storage. Therefore, the problem of HSI denoising has attracted extensive attention from society. Although recent HSI denoising methods provide effective solutions in various optimization directions, their performance under real complex noise is still not optimal. To address these issues, this article proposes a self-modulated cross-attention network that fully utilizes spatial and spectral information. The core of the model is the use of deformable convolution to cross-fuse spatial and spectral features to improve the network denoising capability. At the same time, a self-modulating residual block allows the network to transform features in an adaptive manner based on neighboring bands, improving the network’s ability to deal with complex noise, which we call a feature enhancement block. Finally, we propose a three-segment network architecture that improves the stability of the model. The method proposed in this work outperforms other state-of-the-art methods through comparative analysis of experiments in synthetic and real data.