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Content Provider | IEEE Xplore Digital Library |
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Author | Jian Zhang Ruiqin Xiong Chen Zhao Siwei Ma Debin Zhao |
Copyright Year | 2012 |
Abstract | Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Lap lace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm. |
Starting Page | 592 |
Ending Page | 597 |
File Size | 2375122 |
Page Count | 6 |
File Format | |
ISBN | 9781467316590 |
ISSN | 19457871 |
DOI | 10.1109/ICME.2012.109 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-07-09 |
Publisher Place | Australia |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Gaussian noise Noise measurement TV Image restoration Noise reduction Additives Gaussian-impulse noise Image denoising local and nonlocal consistency mixed noise removal |
Content Type | Text |
Resource Type | Article |
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