Deep Convolutional Network Aided by Non-Local Method for Hyperspectral Image Denoising

Abstract

This paper introduces a new hyperspectral image denoising method called Non-local Convolutional Neural Network Denoiser (NL-CNND). The technique exploits data in four bands adjacent to the target one as additional information for the restoring process, and it uses a pre-denoising step based on BM4D. All the bands paired with their pre-denoised versions in a second step feed a Convolutional Neural Network. To network generalization, one of the inputs is the noise level of the input image, allowing a single model to work with different noise levels. This restoration technique overcomes quality when compared to current eight classical and neural methods. The results show higher peak signal to noise ratio, structural similarity index, and spectral angle mapper metrics than all the other restoration methods, surpassing those achieved using Block Matching and 4D Filtering alone. Besides, the results show a higher level of detail visually while at the same time reducing over-smoothing on the input images’ features. The paper also includes an algorithm for complete image restoration, allowing for denoising full-sized hyperspectral images independent of their shape. The dataset creation used for network training is detailed, based on a small set of available hyperspectral images, encompassing data normalization, conversion, and storage.

Keywords

• Hyperspectral images
• denoising
• BM4D
• convolutive neural network
• NLM
• band correlation