Poisson Noise Reduction with Higher-Order Natural Image Prior Model

doi:10.1137/16M1072930

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Poisson Noise Reduction with Higher-Order Natural Image Prior Model
	Feng, Wensen 1; Qiao, Hong2 ; Chen, Yunjin 3
发表期刊	SIAM JOURNAL ON IMAGING SCIENCES
	2016
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
DOI	10.1137/16M1072930
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	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12572
专题	多模态人工智能系统全国重点实验室_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
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.