DocumentCode :
1323221
Title :
Wavelet-Domain Blur Invariants for Image Analysis
Author :
Makaremi, Iman ; Ahmadi, Majid
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Windsor, Windsor, ON, Canada
Volume :
21
Issue :
3
fYear :
2012
fDate :
3/1/2012 12:00:00 AM
Firstpage :
996
Lastpage :
1006
Abstract :
Radiometric degradation is a common problem in the image acquisition part of many applications. There is much research carried out in an effort to deblur such images. However, it has been proven that it is not always necessary to go through a burdensome process of deblurring. To tackle this problem, different blur-invariant descriptors have been proposed so far, which are either in the spatial domain or based on the properties available in the Fourier domain. In this paper, wavelet-domain blur invariants are proposed for the first time for discrete 2-D signals. These descriptors, which are invariant to centrally symmetric blurs, inherit the advantages that this domain provides. It is also proven that the spatial-domain blur invariants are a special version of the proposed invariants. The performance of these invariants will be demonstrated through experiments.
Keywords :
Fourier transforms; image restoration; wavelet transforms; Fourier domain; centrally symmetric blurs; discrete 2D signals; image acquisition; image analysis; radiometric degradation; spatial domain; wavelet-domain blur invariants; Convolution; Discrete wavelet transforms; Noise; Wavelet analysis; Wavelet domain; Blur moment invariants; centrally symmetric blur; direct analysis; feature extraction; shift-invariant wavelet transform;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2011.2168415
Filename :
6021370
Link To Document :
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