Poisson Noise Reduction with Higher-Order Natural Image Prior Model
Feng, Wensen1; Qiao, Hong2; Chen, Yunjin3
2016
发表期刊SIAM JOURNAL ON IMAGING SCIENCES
卷号9期号:3页码:1502-1524
文章类型Article
摘要Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging, and microscopy. State-of-the-art approaches are clearly dominated by patch-based non local methods in recent years. In this paper, we aim to propose a local Poisson denoising model with both structural simplicity and good performance. To this end, we consider a variational modeling to integrate the so-called fields of experts (FoE) image prior, that has proven an effective higher-order Markov random fields model for many classic image restoration problems. We exploit several feasible variational variants for this task. We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high signal-to-noise ratio (SNR). However, this strategy encounters problem in cases of low SNR. Then we turn to an alternative modeling strategy by using the Anscombe transform and Gaussian statistics derived data term. We retrain the FoE prior model directly in the transform domain. With the newly trained FoE model, we end up with a local variational model providing strongly competitive results against state-of-the-art nonlocal approaches, meanwhile bearing the property of simple structure. Furthermore, our proposed model comes along with an additional advantage, that the inference is very efficient as it is well suited for parallel computation on GPUs. For images of size 512 x 512, our GPU implementation takes less than 1 second to produce state-of-the-art Poisson denoising performance.
关键词Poisson Denoising Fields Of Experts Anscombe Root Transformation Nonconvex Optimization
WOS标题词Science & Technology ; Technology ; Physical Sciences
DOI10.1137/16M1072930
关键词[WOS]INVERSE PROBLEMS ; OPTIMIZATION ; SPARSITY ; RESTORATION ; INTENSITY ; INFERENCE ; MRFS
收录类别SCI
语种英语
WOS研究方向Computer Science ; Mathematics ; Imaging Science & Photographic Technology
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Mathematics, Applied ; Imaging Science & Photographic Technology
WOS记录号WOS:000385277200024
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12572
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
通讯作者Chen, Yunjin
作者单位1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Graz Univ Technol, Inst Comp Graph & Vis, A-8010 Graz, Austria
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Feng, Wensen,Qiao, Hong,Chen, Yunjin. Poisson Noise Reduction with Higher-Order Natural Image Prior Model[J]. SIAM JOURNAL ON IMAGING SCIENCES,2016,9(3):1502-1524.
APA Feng, Wensen,Qiao, Hong,&Chen, Yunjin.(2016).Poisson Noise Reduction with Higher-Order Natural Image Prior Model.SIAM JOURNAL ON IMAGING SCIENCES,9(3),1502-1524.
MLA Feng, Wensen,et al."Poisson Noise Reduction with Higher-Order Natural Image Prior Model".SIAM JOURNAL ON IMAGING SCIENCES 9.3(2016):1502-1524.
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