Year: 2018
Numerical Mathematics: Theory, Methods and Applications, Vol. 11 (2018), Iss. 1 : pp. 49–73
Abstract
Variational methods are an important class of methods for general image restoration. Boosting technique has been shown capable of improving many image denoising algorithms. This paper discusses a boosting technique for general variational image restoration methods. It broadens the applications of boosting techniques to a wide range of image restoration problems, including not only denoising but also deblurring and inpainting. In particular, we combine the recent SOS technique with dynamic parameter to variational methods. The dynamic regularization parameter is motivated by Meyer's analysis on the ROF model. In each iteration of the boosting scheme, the variational model is solved by augmented Lagrangian method. The convergence analysis of the boosting process is shown in a special case of total variation image denoising with a "disk" input data. We have implemented our boosting technique for several image restoration problems such as denoising, inpainting and deblurring. The numerical results demonstrate promising improvement over standard variational restoration models such as total variation based models and higher order variational model as total generalized variation.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/nmtma.OA-2017-0046
Numerical Mathematics: Theory, Methods and Applications, Vol. 11 (2018), Iss. 1 : pp. 49–73
Published online: 2018-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 25
Keywords: Variational method image restoration total variation boosting augmented Lagrangian method.
-
An Efficient Method for Euler’s Elastica Based Image Deconvolution
Wali, Samad | Shakoor, Abdul | Basit, Abdul | Xie, Lipeng | Huang, Chencheng | Li, ChunmingIEEE Access, Vol. 7 (2019), Iss. P.61226
https://doi.org/10.1109/ACCESS.2019.2912660 [Citations: 7] -
A Novel Mesh Denoising Method Based on Relaxed Second-Order Total Generalized Variation
Zhang, Huayan | He, Zhishuai | Wang, XiaochaoSIAM Journal on Imaging Sciences, Vol. 15 (2022), Iss. 1 P.1
https://doi.org/10.1137/21M1397945 [Citations: 3] -
Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond
Panigrahi, Susant Kumar | Tripathy, Santosh Kumar | Bhowmick, Anirban | Satapathy, Santosh Kumar | Barsocchi, Paolo | Bhoi, Akash KumarIEEE Access, Vol. 12 (2024), Iss. P.25090
https://doi.org/10.1109/ACCESS.2024.3364397 [Citations: 4] -
Virtual Viewpoint Film and Television Synthesis Based on the Intelligent Algorithm of Wireless Network Communication for Image Repair
Zhang, Jianfeng | Lv, ZhihanWireless Communications and Mobile Computing, Vol. 2021 (2021), Iss. 1
https://doi.org/10.1155/2021/9063410 [Citations: 2] -
Cover Design of Public Service Advertisement Based on Deep Image Rendering Technology
Yang, Han | Zhang, Longxiang | Wu, Chia-HueiWireless Communications and Mobile Computing, Vol. 2022 (2022), Iss. P.1
https://doi.org/10.1155/2022/8085645 [Citations: 0] -
A Dual Model for Restoring Images Corrupted by Mixture of Additive and Multiplicative Noise
Zhao, Cuicui | Liu, Jun | Zhang, JieIEEE Access, Vol. 9 (2021), Iss. P.168869
https://doi.org/10.1109/ACCESS.2021.3137995 [Citations: 2] -
Enhanced total generalized variation method based on moreau envelope
Zhou, Mengmeng | Zhao, PingMultimedia Tools and Applications, Vol. 80 (2021), Iss. 13 P.19539
https://doi.org/10.1007/s11042-021-10586-9 [Citations: 4] -
A new adaptive boosting total generalized variation (TGV) technique for image denoising and inpainting
Wali, Samad | Zhang, Huayan | Chang, Huibin | Wu, ChunlinJournal of Visual Communication and Image Representation, Vol. 59 (2019), Iss. P.39
https://doi.org/10.1016/j.jvcir.2018.12.047 [Citations: 31]