Hyperspectral Image Denoising by using Hybrid Thresholding Spatio Spectral Total Variation
Jasdeep Kaur , Punjabi university patiala; Er. Bhawna utreja ,Punjabi university patiala; Dr. Charanjit Singh ,Punjabi university patiala
Hyperspectral denoising, Hybrid spatio-spectral total variation (HSSTV), optimization, split-Bregman.
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.
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Paper ID: GRDJEV02I070066
Published in: Volume : 2, Issue : 7
Publication Date: 2017-07-01
Page(s): 92 - 96
Published in: Volume : 2, Issue : 7
Publication Date: 2017-07-01
Page(s): 92 - 96