Exploiting Image Local and Nonlocal Consistency for Mixed Gaussian-Impulse Noise Removal

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 a...

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Bibliographic Details
Main Authors: Jian Zhang, Ruiqin Xiong, Chen Zhao, Siwei Ma, Debin Zhao
Format: Conference Proceeding
Language:English
Subjects:
Additives
Gaussian noise
Gaussian-impulse noise
Image denoising
Image restoration
local and nonlocal consistency
mixed noise removal
Noise measurement
Noise reduction
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Summary: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.
ISSN:1945-7871
1945-788X
DOI:10.1109/ICME.2012.109