Hyperspectral Image Denoising by using Hybrid Thresholding Spatio Spectral Total Variation

This paper introduces a hyperspectral denoising algorithm hinged on hybrid spatio-spectral total variation. The denoising issue have been hatched as a mixed noise diminution issue. A prevalent noise model has been pondered which reckon for not only Gaussian noise but also sparse noise. The inborn composition of hyperspectral images has been manipulated by using 2-D total variation along the spatial dimension and 1-D total variations along the spectral dimensions. The image denoising issues has been contrived as optimization hitch whose results has been acquired using the split-Bregman approach. The proposed method can minimize a remarkable amount of noise from real noisy hyperspectral images which is demonstrated by observational results. The proffer technique has been compared with prevailing avant-garde approaches. The outcomes reveal an excellence of the proposed method in the form of peak signal-to-noise ratio, structural similarity index and the visual quality. Citation: Jasdeep Kaur , Punjabi university patiala; Er. Bhawna utreja ,Punjabi university patiala; Dr. Charanjit Singh ,Punjabi university patiala. "Hyperspectral Image Denoising by using Hybrid Thresholding Spatio Spectral Total Variation." Global Research and Development Journal For Engineering 27 2017: 92 - 96.