A New Multiscale Residual Learning Network for HSI Inconsistent Noise Removal

Hyperspectral image (HSI) often suffers from various noise disturbances which makes the interpretation difficult. To solve this problem, a lot of HSI denoising algorithms have been proposed and widely used. Although many convolution neural network (CNN) based denoising methods have achieved successful performance in independently and identically distributed (i.i.d) Gaussian denoising, they are still limited to remove inconsistent noise, and even worse than traditional representative algorithms like total variation regularized low-rank matrix factorization (LRTV) and low-rank matrix recovery (LRMR). In this letter, a new multiscale residual learning network (MSRHSID) is proposed for HSIs denoising. In this network, a noise estimation network is used in our proposed method to obtain the image noise prior to achieve the reduction of inconsistent noise. At the same time, an efficient multiscale residual module (MRM) is employed to further improve the denoising effect. Both of the denoising experiments on synthetic and real-data HSIs show that this proposed MSRHSID outperforms the state-of-The-Art methods.