Minimum Kurtosis CMA Deconvolution for Blind Image Restoration

Author

Samarasinghe, Pradeepa D. ; Kennedy, Rodney A.

Author_Institution

Res. Sch. of Inf. Sci. & Eng., Australian Nat. Univ., Canberra, ACT

fYear

2008

fDate

12-14 Dec. 2008

Firstpage

271

Lastpage

276

Abstract

A critical assumption in applying Godard CMA algorithm for blind deconvolution and equalization is the assumption of an independently distributed source. Almost all the applications in the literature have based their implementations on this assumption. To our knowledge, no research has been done on the effect of source correlation on adaptive blind deblurring of images through CMA, and this paper addresses that gap, coming up with a novel model of addressing the source correlation problem in the image deblurring through CMA.

Keywords

adaptive signal processing; deconvolution; image restoration; Godard constant modulus algorithm; adaptive blind deblurring; blind deconvolution; blind equalization; blind image restoration; image deblurring; minimum kurtosis constant modulus algorithm deconvolution; source correlation; Adaptive systems; Blind equalizers; Computer science; Context; Deconvolution; Educational institutions; Image processing; Image restoration; Signal processing; Signal processing algorithms; CMA; Constant Modulus Algorithm; Godard algorithm; blind deconvolution; blind equalization; blind image restoration; image processing; kurtosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation for Sustainability, 2008. ICIAFS 2008. 4th International Conference on

Conference_Location

Colombo

Print_ISBN

978-1-4244-2899-1

Electronic_ISBN

978-1-4244-2900-4

Type

conf

DOI

10.1109/ICIAFS.2008.4783974

Filename

4783974