Image restoring method based on region selection network and its application in computational imaging

doi:10.37188/OPE.20212904.0864

CIOMP OpenIR

	Image restoring method based on region selection network and its application in computational imaging
	X.-T. Wu; H. Yang and X.-L. Sun
	2021
发表期刊	Guangxue Jingmi Gongcheng/Optics and Precision Engineering
ISSN	1004924X
卷号	29 期号:4 页码:864-876
摘要	Computational imaging is a new interdisciplinary subject that has gained widespread research attention in recent years. However, its efficiency and recovery effect restrict its development in engineering applications. In this paper, an efficient image deblurring method based on a region selection network is proposed to tackle the restoration task in the fields of wavefront coding imaging and single lens computational imaging. In contrast to traditional image restoration methods, which usually involve construction of an objective function and addition of reasonable image priors to restore blurry images, the proposed method is based on a combination of a deep learning method and a traditional restoration algorithm. The traditional method is used for the main image restoration process, while the deep learning method is used to intervene in the kernel estimation region selection. The deep learning method involves constructing and training a deep binary classification network, which can automatically eliminate the flat overexposure, short texture, and other areas in the global image, and select the most suitable block area for kernel estimation. On this basis, traditional restoration methods perform kernel estimation, non-blind image restoration, and image enhancement processing. The experimental results show that the proposed method can achieve a good and stable restoration effect, that the proposed region selection method can reduce the computational complexity, and that the point spread function can be estimated well. When the error rate is limited to 1.5, the restoration success rate is improved by at least 2.1%, and the average peak signal-to-noise ratio (PSNR) is increased by at least 0.5 dB. 2021, Science Press. All right reserved.
DOI	10.37188/OPE.20212904.0864
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收录类别	EI
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文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65314
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	X.-T. Wu,H. Yang and X.-L. Sun. Image restoring method based on region selection network and its application in computational imaging[J]. Guangxue Jingmi Gongcheng/Optics and Precision Engineering,2021,29(4):864-876.
APA	X.-T. Wu,&H. Yang and X.-L. Sun.(2021).Image restoring method based on region selection network and its application in computational imaging.Guangxue Jingmi Gongcheng/Optics and Precision Engineering,29(4),864-876.
MLA	X.-T. Wu,et al."Image restoring method based on region selection network and its application in computational imaging".Guangxue Jingmi Gongcheng/Optics and Precision Engineering 29.4(2021):864-876.

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