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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2020 - 10

Improved Edge Refinement Filter with Entropy Feedback Measurement for Retrieving Region of Interest and Blind Image Deconvolution

MOHD SHAPRI, A. H. See more information about MOHD SHAPRI, A. H. on SCOPUS See more information about MOHD SHAPRI, A. H. on IEEExplore See more information about MOHD SHAPRI, A. H. on Web of Science, ABDULLAH, M. Z. See more information about ABDULLAH, M. Z. on SCOPUS See more information about ABDULLAH, M. Z. on SCOPUS See more information about ABDULLAH, M. Z. on Web of Science
 
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Download PDF pdficon (860 KB) | Citation | Downloads: 678 | Views: 1,840

Author keywords
image restoration, image edge detection, deconvolution, filtering, image enhancement

References keywords
image(14), processing(6), pattern(6), images(6), deblurring(6), blind(6), edge(5), detection(5), analysis(5), vision(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-02-28
Volume 20, Issue 1, Year 2020, On page(s): 71 - 82
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.01010
Web of Science Accession Number: 000518392600010
SCOPUS ID: 85083728691

Abstract
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This study proposes an improved edge refinement filter with entropy feedback measurement for locating an optimal region of interest (ROI) in blurry images. This technique is inspired by He et al.'s algorithm and enhanced by introducing a suitable filter to obtain smooth unwanted pixels whilst retaining important and significant edges. This approach led to an accurate retrieval of ROI and a considerably precise image restoration within a blind deconvolution framework. Results show that the proposed method is more competitive than existing techniques and achieves better performance in terms of peak signal-to-noise ratio, kernel similarity index and error ratio.


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

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References Weight

Web of Science® Citations for all references: 21,180 TCR
SCOPUS® Citations for all references: 19,461 TCR

Web of Science® Average Citations per reference: 683 ACR
SCOPUS® Average Citations per reference: 628 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 2024-04-18 15:03 in 173 seconds.




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