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The application of multivariate data analysis methods such as ICA to solve the blind deconvolution problem requires the source images to be statistically independent. Since this is not always true, a subband decomposition approach is taken. Here it is assumed that the wideband source signals are dependent, but there exist some narrow subbands where they are independent. These subbands are determined by finding those subbands with minimum mutual information between corresponding nodes of the subband decomposition scheme. Subband decomposition is brought about by undecimated wavelet transform as well as Gabor wavelets. Patches are selected randomly from these subband images and given as inputs to the ICA algorithm. The ICA algorithm gives as its output the independent components which resemble short edges and capture the blurring information in the image around edges and corners. These are used as PSFs given to the blind Richardson-Lucy algorithm for deconvolution of the blurred image. The results obtained are comparable to those obtained by the blind Richardson-Lucy algorithm.