@Article{JCM-39-1,
author = {Miyoun, Jung and Myungjoo, Kang},
title = {Image Restoration under Cauchy Noise with Sparse Representation Prior and Total Generalized Variation},
journal = {Journal of Computational Mathematics},
year = {2021},
volume = {39},
number = {1},
pages = {81--107},
abstract = {<p style="text-align: justify;">This article introduces a novel variational model for restoring images degraded by
Cauchy noise and/or blurring. The model integrates a nonconvex data-fidelity term with
two regularization terms, a sparse representation prior over dictionary learning and total
generalized variation (TGV) regularization. The sparse representation prior exploiting
patch information enables the preservation of fine features and textural patterns, while
adequately denoising in homogeneous regions and contributing natural visual quality. TGV
regularization further assists in effectively denoising in smooth regions while retaining
edges. By adopting the penalty method and an alternating minimization approach, we
present an efficient iterative algorithm to solve the proposed model. Numerical results
establish the superiority of the proposed model over other existing models in regard to
visual quality and certain image quality assessments.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.1907-m2018-0234},
url = {https://global-sci.com/article/84219/image-restoration-under-cauchy-noise-with-sparse-representation-prior-and-total-generalized-variation}
}