research-article

An Image Denoising Fast Algorithm for Weighted Total Variation

Authors:

Shuai ShaoAuthors Info & Claims

ICIIP '17: Proceedings of the 2nd International Conference on Intelligent Information Processing

Article No.: 16, Pages 1 - 7

https://doi.org/10.1145/3144789.3144809

Published: 17 July 2017 Publication History

Abstract

The total variation (TV) model is a classical and effective model in image denoising, but the weighted total variation (WTV) model has not attracted much attention. In this paper, we propose a new constrained WTV model for image denoising. A fast denoising dual method for the new constrained WTV model is also proposed. To achieve this task, we combines the well known gradient projection (GP) and the fast gradient projection (FGP) methods on the dual approach for the image denoising problem. Experimental results show that the proposed method outperforms currently known GP andFGP methods, and canbe applicable to both the isotropic and anisotropic WTV functions.

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Cited By

Sghaier MChouzenoux EPesquet JMuller S(2020)A New Spatially Adaptive TV Regularization for Digital Breast Tomosynthesis2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)10.1109/ISBI45749.2020.9098497(629-633)Online publication date: Apr-2020
https://doi.org/10.1109/ISBI45749.2020.9098497
Aggarwal SSingh NMishra K(2020)Digital Image Analysis Is a Silver Bullet to COVID-19 PandemicComputational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis10.1007/978-981-15-8534-0_21(397-414)Online publication date: 17-Oct-2020
https://doi.org/10.1007/978-981-15-8534-0_21

Index Terms

An Image Denoising Fast Algorithm for Weighted Total Variation
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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Abstract
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cover image ACM Other conferences

ICIIP '17: Proceedings of the 2nd International Conference on Intelligent Information Processing

July 2017

211 pages

ISBN:9781450352871

DOI:10.1145/3144789

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Wanfang Data: Wanfang Data, Beijing, China
International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

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Published: 17 July 2017

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IIP'17

IIP'17: 2017 2nd International Conference on Intelligent Information Processing

July 17 - 18, 2017

Bangkok, Thailand

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ICIIP '17 Paper Acceptance Rate 32 of 202 submissions, 16%;

Overall Acceptance Rate 87 of 367 submissions, 24%

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Cited By

Sghaier MChouzenoux EPesquet JMuller S(2020)A New Spatially Adaptive TV Regularization for Digital Breast Tomosynthesis2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)10.1109/ISBI45749.2020.9098497(629-633)Online publication date: Apr-2020
https://doi.org/10.1109/ISBI45749.2020.9098497
Aggarwal SSingh NMishra K(2020)Digital Image Analysis Is a Silver Bullet to COVID-19 PandemicComputational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis10.1007/978-981-15-8534-0_21(397-414)Online publication date: 17-Oct-2020
https://doi.org/10.1007/978-981-15-8534-0_21

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