Proposing a Continuous Spectral-Spatial Sparse Representation Model for Denoising Hyperspectral Images

Abstract

Due to its high capability in acquiring spectral and spatial information, hyperspectral imaging technology has gained significant attention in remote sensing. However, in practice, it is impossible to avoid noise in hyper spectral images due to camera artifacts and the external environment during the acquisition and transmission process. The presence of noise in these images hinders the detection of subtle differences between different materials in the image. Therefore, it is crucial to minimize noise as much as possible before performing any analysis and interpretation. Removing noise from hyperspectral images is a crucial preprocessing step that enhances image quality for various applications, including object recognition and classification. The challenge arises when we need to remove additive white mean-spherohomogeneous Gaussian noise from the given image. Previous research has suggested that thinning the noise-free parts of the image can be effective in removing noise. This article aims to implement the method proposed in using a programming language. The method involves extracting intra-band structure and inter-band correlation while displaying the common tank and learning the common dictionary. In the continuous thin coding phase, the inter-band correlation is extracted to maintain the same structure and achieve spectrum continuity. In contrast, the intra-band structure is used to encode differences in the spatial structure of different bands. Furthermore, a joint dictionary training algorithm is used to obtain a dictionary that simultaneously describes the content of different bands. This ensures that the resulting dictionary preserves the inter-band correlations and enhances the noise-removal process.