Hyperspectral Image Denoising Based on Principal-Third-Order-Moment Analysis

Abstract

Denoising serves as a critical preprocessing step for the subsequent analysis of the hyperspectral image (HSI). Due to their high computational efficiency, low-rank-based denoising methods that project the noisy HSI into a low-dimensional subspace identified by certain criteria have gained widespread use. However, methods employing second-order statistics as criteria often struggle to retain the signal of the small targets in the denoising results. Other methods utilizing high-order statistics encounter difficulties in effectively suppressing noise. To tackle these challenges, we delve into a novel criterion to determine the projection subspace, and propose an innovative low-rank-based method that successfully preserves the spectral characteristic of small targets while significantly reducing noise. The experimental results on the synthetic and real datasets demonstrate the effectiveness of the proposed method, in terms of both small-target preservation and noise reduction.

Memory Augmentation and Non-Local Spectral Attention for Hyperspectral Denoising

“…Benefiting from the richness of spectral information, HSIs play a crucial role in earth observation, such as target detection [4], mineral exploration [5], image classification [6], and more. However, due to the complexity and uncertainty of imaging, HSIs inevitably suffer from noise interference, including Gaussian noise, striping noise, and mixed noise [7]. The presence of image noise reduces image quality and affects the interpretation of target information, which greatly hinders the application of hyperspectral images.…”

“…In recent years, a large number of denoising algorithms have been proposed for HSIs disturbed by noises. According to the solution method, these can be divided into three categories, which are filtering-based denoising methods, optimization-based denoising methods, and deep learning-based denoising methods [7][8][9].…”

Hyperspectral Image Denoising Based on Deep and Total Variation Priors

“…These noise sources contribute to the degradation of hyperspectral images during the acquisition process, often manifesting as a combination of different types of noise such as Gaussian noise [6], impulse noise, deadlines, stripes, and others [7]. Moreover, atmospheric turbulence and system movement cause blurring in HSIs, leading to a mixture of different degradation types [8][9][10][11][12][13]. Consequently, there is an urgent need to enhance the quality of and reduce the noise in HSI before its application in various fields.…”