Spectral Non-gaussianity for Blind Image Deblurring
A blind image deblurring method based on a new non-gaussianity measure and independent component analysis is presented. The scheme assumes independency among source signals (image and filter function) in the frequency domain. According to the Central Limit Theorem the blurred image becomes more Gaussian. The original image is assumed to be non-gaussian and using a spectral non-gaussianity measure (kurtosis or negentropy) one can estimate an inverse filter function that maximizes the non-gaussianity of the deblurred image. A genetic algorithm (GA) optimizing the kurtosis in the frequency domain is used for the deblurring process. Experimental results are presented and compared with some existing methods. The results show that the deblurring from the spectral domain offers several advantages over that from the spatial domain.
KeywordsIndependent Component Analysis Point Spread Function Independent Component Analysis Motion Blur Blind Deconvolution
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