NDLI: Blind deconvolution using a nondimensional Gaussianity measure

Content Provider	IEEE Xplore Digital Library
Author	Xu Zhou Fugen Zhou Xiangzhi Bai
Copyright Year	2013
Description	Author affiliation: Sch. of Astronaut., Beihang Univ., Beijing, China (Xu Zhou; Fugen Zhou; Xiangzhi Bai)
Abstract	Blind image deconvolution (BID) is a severely ill-posed problem which requires prior information on the latent image to estimate the blur kernel. In this paper, a new observation that blurring always pushes the gradient of a local image region toward its mean value is introduced. And we formulate a novel function to measure the distance between the local gradient and its mean value. A novel regularizer associated with local gradient means is proposed. As it requires to segment the whole image into small regions, we propose an approximate method without any segmentation. Thanks to its simplicity the algorithm is fast and robust. Numerous experimental results on synthetic and real data demonstrate that our method is capable of removing various uniform blurs such as motion blur, atmospheric blur and out-of-focus blur.
Starting Page	877
Ending Page	881
File Size	3742487
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Deconvolution Image edge detection Estimation Image segmentation Cameras Electric shock out-of-focus blur Image restoration blind deconvolution motion blur atmospheric blur
Content Type	Text
Resource Type	Article

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