Lp-norm-based successive denoising approach for hyperspectral images. Issue 4 (3rd April 2023)

• Record Type: Journal Article
• Authors: Maji, Suman Kumar; Aetesam, Hazique
• Is Part Of: Remote sensing letters. Volume 14:Issue 4(2023)
• Page Start: 334
• Page End: 345
• Publication Date: 2023-04-03
• Subjects: Hyperspectral imaging -- denoising -- ℓp−norm -- half quadratic splitting; Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2023.2195567
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: Hyperspectral images (HSI) are essentially a data cube containing hundreds of stacked 2-D images collected over a set of adjacent spectral bands with the idea of obtaining a complete spectral information for each pixel in a scene. However, multiple factors contribute to noise, which manifests as grainy textures, dead pixels and stripes in the acquired images, and is classically modelled as a mixture of Gaussian and impulse noise statistics. Denoising therefore forms an important pre-processing task to most HSI applications. In this paper, we propose a novel ℓ p norm-based successive denoising strategy for the removal of mixed Gaussian-impulse noise in HSI. The image formation model is split into two different parts. The proposed method is a Bayesian-derived variational model designed to handle the removal of Gaussian and impulse noise in a successive manner, and is solved using the half-quadratic split approach. Comparison with various existing HSI denoising techniques, both qualitatively and quantitatively, suggest the superior denoising ability of the proposed method compared to the rest.