Hyperspectral Image Denoising with Discrete Cosine Transform and CNN Denoiser

Lingsheng Wu, Rui Wang, Shaoguang Huang

Abstract: Hyperspectral image (HSI) captures rich spectral information in more than hundreds of spectral bands, which allows far better discrimination between ground objects compared with the conventional optical images. Therefore, HSIs find a number of applications in Earth observation, precision agriculture and environmental monitoring. However, due to the effect of poor imaging condition, hardware limitation and sensor noise, the acquisition of HSIs is inevitably affected by noise, which hinders accurate interpretation of HSI in real applications. A number of denoising methods have been proposed for HSI, including BM4D [1] , LRMR [2] , LRTV [3] . Recent works such as HSID-CNN [4] , which adopt deep learning based technique, have yielded the advanced performance. However, most of them design neural network architecture to process noisy data in a spatial domain. It is known that HSI has significantly different properties in high-frequency and low-frequency domains. For instance, in low-frequency domain, smoothing regions of HSI are more relevant while the details of HSI and noise are often more salient in the high-frequency domain. Current methods restore clean HSIs in a spatial domain, and thus neglect the properties of HSI in different frequency domains, which lead to limited denoising performance.