NDLI: Variational total curvature model for multiplicative noise removal

Content Provider	IET Digital Library
Author	Xu, Xinli Yu, Teng Xu, Xinmei Hou, Guojia Liu, Ryan Wen Pan, Huizhu
Abstract	The multiplicative noise removal problem has received considerable attention recently. To solve this problem, various variational models have been proposed, which minimise an energy functional composed of the data term and the regularisation term. Regarding the regularisation term, a first-order model is frequently used to remove multiplicative noise, which may cause staircase effect and loss of contrast in the output image. In this study, the authors use a second-order model, the total curvature (TC), to solve the above problem. The TC model has the benefit of removing the staircase effect and maintaining image edges, contrasts and corners. The augmented Lagrange method is utilised to solve the proposed TC model by introducing auxiliary variables, Lagrange multipliers and using alternating optimisation strategy. In each loop of optimisation, the fast Fourier transform, generalised soft threshold formulas, projection method and gradient descent method are integrated effectively. The experimental results show that the TC model can effectively remove staircase effect and preserve smoothness, via comparison with the first-order model (total variation regularisation and Perona–Malik regularisation). Furthermore, the TC model is better than another second-order model based on bounded Hessian regularisation in preserving contrast and corner.
Starting Page	542
Ending Page	552
Page Count	11
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 4, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0332
Journal	IET Computer Vision
Publisher Date	2018-01-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Alternating Optimisation Strategy Augmented Lagrange Method Auxiliary Variables Computer Vision And Image Processing Technique Fast Fourier Transform First-order Model Generalised Soft Threshold Formulas Gradient Descent Method Gradient Method Image Denoising Integral Transforms Interpolation And Function Approximation Lagrange Multipliers Multiplicative Noise Removal Problem Numerical Analysis Optical, Image And Video Signal Processing Optimisation Optimisation Technique Projection Method Second-order Model TC Model Variational Total Curvature Model
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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