NDLI: Self-adaptive blind super-resolution image reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Yunfei Bai Jing Hu Yupin Luo
Copyright Year	2010
Description	Author affiliation: Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation Tsinghua University, Beijing, China 10084 (Yunfei Bai; Jing Hu; Yupin Luo)
Abstract	Super-resolution (SR) image reconstruction is a rapidly developing area in image processing. Especially, blind SR can generate high space resolution image without requiring priori information of the point spread function (PSF). In this paper, we propose a self-adaptive blind super-resolution image reconstruction approach which is based on multiple images. Our method can adaptively choose the parameter of regularization term. The quality of resulting image especially in the respect of edge-preserving property is better than approaches such as maximum a posteriori estimation (MAP) tested with practical examples. We employ Lorentzian function as spread coefficient and partial differential function as regularization term of resulting image. A generalized version of the eigenvector-based alternating minimization (EVAM) constraint is used to regularize PSF and estimate resulting image and PSF simultaneously. In addition, in order to achieve self-adaptive regularization terms parameter choosing, we also present a new robust no-reference image quality assessment method which provides blurring and ringing effect assessment value as feedback.
Starting Page	1208
Ending Page	1212
File Size	521103
Page Count	5
File Format	PDF
ISBN	9781424465132
e-ISBN	9781424465163
DOI	10.1109/CISP.2010.5647225
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image quality Strontium Image resolution Image edge detection blind SR no-reference image quality assessment Mathematical model super-resolution image reconstruction Image reconstruction Signal resolution
Content Type	Text
Resource Type	Article

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