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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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An Adaptive Parameter Estimation in a BTV Regularized Image Super-Resolution Reconstruction

MOFIDI, M. See more information about MOFIDI, M. on SCOPUS See more information about MOFIDI, M. on IEEExplore See more information about MOFIDI, M. on Web of Science, HAJGHASSEM, H. See more information about  HAJGHASSEM, H. on SCOPUS See more information about  HAJGHASSEM, H. on SCOPUS See more information about HAJGHASSEM, H. on Web of Science, AFIFI, A. See more information about AFIFI, A. on SCOPUS See more information about AFIFI, A. on SCOPUS See more information about AFIFI, A. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,752 KB) | Citation | Downloads: 592 | Views: 356

Author keywords
image processing, image reconstruction, maximum a posteriori, spatial resolution, statistical analysis

References keywords
image(35), resolution(24), super(23), process(16), signal(11), reconstruction(10), regularization(8), zhang(6), robust(6), restoration(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03001
Web of Science Accession Number: 000410369500001
SCOPUS ID: 85028561933

Abstract
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Access to the fine spatial resolution has always been a hotspot in digital imaging. One way to improve resolution is to use signal post-processing techniques. In this study, an improved multi-frame image super-resolution (SR) algorithm is proposed. The objective function should be minimized consists of a data error term, a regularization term and a regularization parameter. Based on the bilateral-total-variation (BTV) regularization, in the proposed method on one hand, the data error term incorporates frames with high accuracies in the reconstruction process, where an indicator weights each frame proportional to the frame error. On the other hand the regularization parameter is updated in each iteration based upon the Morozov's discrepancy principle. Iterative adjustment of the regularization parameter guarantees the SR solution to satisfy discrepancy principle. Visual evaluation and also quantitative measurements show that the performance of the proposed algorithm is better than of the several state-of-the-art methods.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 10,927 TCR
SCOPUS® Citations for all references: 15,872 TCR

Web of Science® Average Citations per reference: 295 ACR
SCOPUS® Average Citations per reference: 429 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-12-13 03:52 in 210 seconds.




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