Poisson Noise Reduction with Higher-Order Natural Image Prior Model
文献类型：期刊论文
| 作者 | Feng, Wensen1; Qiao, Hong2 ; Chen, Yunjin3
|
| 刊名 | SIAM JOURNAL ON IMAGING SCIENCES
 |
| 出版日期 | 2016 |
| 卷号 | 9期号:3页码:1502-1524 |
| 关键词 | Poisson denoising fields of experts Anscombe root transformation nonconvex optimization |
| 通讯作者 | Chen, Yunjin |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology ; Physical Sciences |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Mathematics, Applied ; Imaging Science & Photographic Technology |
| 研究领域[WOS] | Computer Science ; Mathematics ; Imaging Science & Photographic Technology |
| 关键词[WOS] | INVERSE PROBLEMS ; OPTIMIZATION ; SPARSITY ; RESTORATION ; INTENSITY ; INFERENCE ; MRFS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000385277200024 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12572]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 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 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：自动化研究所
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