Lp-norm-based successive denoising approach for hyperspectral images

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.