Open AccessProximal approach to denoising hyperspectral images under mixed‐noise model
Author(s) -
Aetesam Hazique,
Poonam Kumari,
Maji Suman Kumar
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1763
Subject(s) - hyperspectral imaging , noise reduction , pattern recognition (psychology) , artificial intelligence , total variation denoising , computer science , gaussian noise , tikhonov regularization , noise (video) , mathematics , impulse noise , maximum a posteriori estimation , image (mathematics) , algorithm , inverse problem , maximum likelihood , statistics , mathematical analysis , pixel
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.