Samarasinghe, PradeepaKennedy, Rodney2015-12-13December 19781424479078http://hdl.handle.net/1885/83579In this paper, we analyze the applicability of Constant Modulus Algorithm (CMA), one of the most widely used and tested blind equalization technique to blind image deconvolution. With a detailed mathematical analysis, we show that the strong correlation between the neighboring spatial locations found in natural images becomes a major constraint on the convergence of CMA. In order to overcome this constraint, we introduce a novel image pixel correlation model in relation with natural image statistics. Based on this model, a segmented blind image deconvolution through CMA is proposed. The robustness of the proposed algorithm with natural images is discussed in terms of efficiency and effectiveness.Keywords: Blind image deconvolution; Constant modulus algorithms; Equalization; Image correlations; Kurtosis; Meso-Kurtic; Natural image statistics; Stationary points; Whitening; Algorithms; Blind equalization; Communication systems; Convolution; Deconvolution; Dis Blind image deconvolution; Constant modulus algorithm; Equalization; Image correlation; Kurtosis; Meso-Kurtic; Natural image statistics; Stationary points; Whitening201010.1109/ICSPCS.2010.57097122016-02-24