A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal From Images on Graphs
被引:40
作者:
Wang, Cong
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机构:
Xidian Univ, Sch Electromech Engn, Xian 710071, Peoples R ChinaXidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China
Wang, Cong
[1
]
Yan, ZiYue
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机构:
Univ Southern Calif, Dept Comp Sci, Viterbi Sch Engn, Los Angeles, CA 90007 USAXidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China
Yan, ZiYue
[2
]
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机构:
Pedrycz, Witold
[1
,3
,4
]
Zhou, MengChu
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机构:
Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USAXidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China
Zhou, MengChu
[5
,6
]
Li, ZhiWu
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机构:
Xidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China
Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R ChinaXidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China
Li, ZhiWu
[1
,5
]
机构:
[1] Xidian Univ, Sch Electromech Engn, Xian 710071, Peoples R China
[2] Univ Southern Calif, Dept Comp Sci, Viterbi Sch Engn, Los Angeles, CA 90007 USA
Image denoising technologies in a Euclidean domain have achieved good results and are becoming mature. However, in recent years, many real-world applications encountered in computer vision and geometric modeling involve image data defined in irregular domains modeled by huge graphs, which results in the problem on how to solve image denoising problems defined on graphs. In this paper, we propose a novel model for removing mixed or unknown noise in images on graphs. The objective is to minimize the sum of a weighted fidelity term and a sparse regularization term that additionally utilizes wavelet frame transform on graphs to retain feature details of images defined on graphs. Specifically, the weighted fidelity term with -norm is designed based on a analysis of the distribution of mixed noise. The augmented Lagrangian and accelerated proximal gradient methods are employed to achieve the optimal solution to the problem. Finally, some supporting numerical results and comparative analyses with other denoising algorithms are provided. It is noted that we investigate image denoising with unknown noise or a wide range of mixed noise, especially the mixture of Poisson, Gaussian, and impulse noise. Experimental results reported for synthetic and real images on graphs demonstrate that the proposed method is effective and efficient, and exhibits better performance for the removal of mixed or unknown noise in images on graphs than other denoising algorithms in the literature. The method can effectively remove mixed or unknown noise and retain feature details of images on graphs. It delivers a new avenue for denoising images in irregular domains.