NDLI: Proximal approach to denoising hyperspectral images under mixed-noise model

Content Provider	IET Digital Library
Author	Aetesam, Hazique Poonam, Kumari Maji, Suman Kumar
Abstract	The authors present a proximal approach to hyperspectral image denoising adapted to the mixed noise behaviour of hyperspectral data; named hyperspectral image proximal denoiser (HSIProxDenoiser). A combination of Gaussian-impulse noise has been handled under maximum a posteriori framework using two data fidelity terms. They have incorporated prior information about the data in the form of two regularisation terms, namely Tikhonov–Miller (TM) and total variation (TV). Since TV possesses feature selection capability by setting some of the coefficients to zero, it works well when there are a small number of significant features. On the other hand, TM works well if there are a large number of similar features. Hence, including both regularisation terms can help achieve the desired denoising performance. The resultant optimisation problem is solved using a variant of primal-dual hybrid gradient by splitting the former into different functions and calculating their proximal operators individually. Experimental results over both synthetic as well as real hyperspectral image data validate the potential of the proposed technique both visually and in terms of quantitative metrics.
Starting Page	3366
Ending Page	3372
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1763
Journal	IET Image Processing
Publisher Date	2020-08-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Data Fidelity Term Denoising Performance Feature Selection Feature Selection Capability Gaussian Noise Gaussian-impulse Noise Gradient Method HSIProxDenoiser Hyperspectral Data Hyperspectral Image Hyperspectral Image Data Hyperspectral Image Proximal Denoiser Image Denoising Impulse Noise Maximum A Posteriori Framework Maximum Likelihood Estimation Mixed-noise Model Optical, Image And Video Signal Processing Optimisation Optimisation Problem Optimisation Technique Primal-dual Hybrid Gradient Variant Proximal Approach Proximal Operators Quantitative Metrics Statistics Tikhonov–Miller Regularisation Terms Total Variation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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