Original Article: A new nonlocal total variation regularization algorithm for image denoising

Authors:
Xinwu Liu;Lihong Huang
Affiliations:
-;-
Venue:
Mathematics and Computers in Simulation
Year:
2014

Citing 17
Cited 0

Nonlinear total variation based noise removal algorithms

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The Split Bregman Method for L1-Regularized Problems

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A New Alternating Minimization Algorithm for Total Variation Image Reconstruction

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Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction

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Nonlinear inverse scale space methods for image restoration

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Noise removal using smoothed normals and surface fitting

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Abstract

The staircasing effect inevitably emerges in the recovered image via the local total variation (TV) based methods. To overcome this drawback, this paper elaborates on a novel nonlocal TV scheme associated with the quadratic perturbation of the ROF model for noise removal. Computationally, we present an improved split Bregman algorithm for minimizing the proposed energy functional recursively. Experimental results clearly demonstrate that our proposed strategy outperforms the corresponding TV scheme, especially in possessing higher computation speed and preserving the textures and fine details better when image denoising.