NDLI: Adaptive regularization with Lorentzian norm for image superresolution

Content Provider	IEEE Xplore Digital Library
Author	Aiye Shi Lizhong Xu Wenbo Si
Copyright Year	2010
Description	Author affiliation: College of Computer and Information Engineering, Hohai University, Nanjing, China (Aiye Shi; Lizhong Xu; Wenbo Si)
Abstract	Super-resolution reconstruction (SRR) is an effective approach for improving spatial resolution of image, which does not need change the original imaging system hardware. By introducing appropriate regularization term in the image SRR, the edge of the reconstructed image can be preserved while noise amplification being restrained. In addition, the choice of error term is important for SRR. However, how to construct a suitable cost function and regularization parameter had been an open question. In this paper, we propose an improved SRR method with Lorentzian norm combining adaptive regularization. The adaption of Lorentzian norm can resolve outlier problem and preserve edge of image. Furthermore, by adaptively selecting regularization parameter in the proposed method can avoid the randomness of trial and error method. Experimental results are on standard images show that the proposed method is effective.
Starting Page	3502
Ending Page	3505
File Size	434451
Page Count	4
File Format	PDF
ISBN	9781424476169
e-ISBN	9781424476183
DOI	10.1109/ICISE.2010.5690127
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Super-resolution reconstruction Image resolution Image edge detection Image processing Adaptive regularization Noise Bismuth Cost function Lorentzian norm Image reconstruction Signal resolution
Content Type	Text
Resource Type	Article

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