  Hyperspectral images (HSIs) have been widely applied in many fields, such as
military, agriculture, and environment monitoring. Nevertheless, HSIs commonly
suffer from various types of noise during acquisition. Therefore, denoising is
critical for HSI analysis and applications. In this paper, we propose a novel
blind denoising method for HSIs based on Multi-Stream Denoising Network
(MSDNet). Our network consists of the noise estimation subnetwork and denoising
subnetwork. In the noise estimation subnetwork, a multiscale fusion module is
designed to capture the noise from different scales. Then, the denoising
subnetwork is utilized to obtain the final denoising image. The proposed MSDNet
can obtain robust noise level estimation, which is capable of improving the
performance of HSI denoising. Extensive experiments on HSI dataset demonstrate
that the proposed method outperforms four closely related methods.
