NIEG-PNet: noise intensity estimation guided progressive network for hyperspectral image denoising

Abstract

ABSTRACT How to remove various kinds of noises of hyperspectral images (HSIs) simultaneously is an important issue that we face, which will directly affect the subsequent application of HSIs. Many HSI denoising techniques have been proposed for this purpose and deep convolutional neural network (CNN) is the most effective approach in recent years. However, existing HSI denoising methods are usually unsatisfactory in removing mixed noises and preserving more details. In this paper, we propose a novel noise intensity estimation guided progressive network (NIEG-PNet) to address these problems. To be specific, we design three core modules to improve the HSI denoising accuracy. First, we propose a noise intensity estimation module (NIEM) to capture multiscale noises and structural characteristics of HSI, which can also be a noise prior to guide the network learning. Second, we propose a recurrent feedback grouped denoising module (RFGDM) to fully capture the spectral correlation. Moreover, we design a dual self-attention module including a position self-attention module and a channel self-attention module to exploit the local and global spatial and spectral correlations and alleviate the problem of small available training HSIs. Last, we propose a global spatial-spectral consistency module (GSSCM), which designs a new parallel structure to combine the two-dimensional and the three-dimensional convolutions more effectively. Moreover, it can explore the relationship between the spectral and the horizontal or vertical direction of the spaces. The experimental results on both synthetic and real-data experiments show the superiority of the proposed NIEG-PNet compared to other traditional and advanced HSI denoising methods.