Hyperspectral Image Denoising Based on Parallel Cross-Fusion Network

Hyperspectral images (HSIs) are widely used in agriculture and environmental monitoring. However, due to the climate and weather conditions, the acquired images commonly contain noise or detail loss, which greatly affects the image interpretation. To solve the problem, we propose a HSI denoising network based on the combination of Transformer and convolutional neural network (CNN), which can effectively improve the complex noise while ensuring image quality by using the powerful local analysis ability of CNN and the effective global feature interaction of Transformer. Extensive experiments on HSI dataset show that the proposed method outperforms four closely related methods.