Infrared star image denoising using regions with deep reinforcement learning

doi:10.1016/j.infrared.2021.103819

CIOMP OpenIR

	Infrared star image denoising using regions with deep reinforcement learning
	Z. Zhang; W. Zheng; Z. Ma; L. Yin; M. Xie and Y. Wu
	2021
发表期刊	Infrared Physics and Technology
ISSN	13504495
卷号	117
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.
DOI	10.1016/j.infrared.2021.103819
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收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	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).

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