Infrared star image denoising using regions with deep reinforcement learning

doi:10.1016/j.infrared.2021.103819

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	Infrared star image denoising using regions with deep reinforcement learning
	Z. Zhang; W. Zheng; Z. Ma; L. Yin; M. Xie and Y. Wu
刊名	Infrared Physics and Technology
	2021
卷号	117
ISSN号	13504495
DOI	10.1016/j.infrared.2021.103819
英文摘要	Efficient and accurate extraction and restoration of star targets in infrared star images with small number of frames is a growing need for optical adaptive image processing. Among the various noise in star images, mixed Poisson-Gaussian noise is difficult to be accurately suppressed due to its complicated distribution function. Aiming at obtaining the true value of star targets signal intensity in infrared images, a novel star target extraction and denoising model called regions with deep reinforcement learning (RDRL) is designed and developed in this study. This fully-automatic model contains two modules: (1) star region extraction module (SREM) that generates star regions within the image through an iterative algorithm based on geometric centroid method (GCM); (2) denoising module that performs an iterative denoising process on the star regions based on deep reinforcement learning. The denoising algorithm is tested on infrared star images, and the experiment results indicate that the proposed RDRL denoising model is able to achieve more accurate restoration with a smaller number of calculations than existing star image denoising methods. 2021 Elsevier B.V.
URL标识	查看原文
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/65345]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Z. Zhang,W. Zheng,Z. Ma,et al. Infrared star image denoising using regions with deep reinforcement learning[J]. Infrared Physics and Technology,2021,117.
APA	Z. Zhang,W. Zheng,Z. Ma,L. Yin,&M. Xie and Y. Wu.(2021).Infrared star image denoising using regions with deep reinforcement learning.Infrared Physics and Technology,117.
MLA	Z. Zhang,et al."Infrared star image denoising using regions with deep reinforcement learning".Infrared Physics and Technology 117(2021).