A Novel Iterative Blind Deconvolution Using Morphology

Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel or point spread function is unknown. Despite of exhaustive research over the last few decades, blind image deconvolution still remains an unsolved problem. In this paper, we present a novel morphology based initial estimation technique of true image for the Iterative Blind Deconvolution (IBD) of linearly degraded images without the explicit knowledge of either the original image or the point spread function. The only constraints imposed are the non-negativity and finite support size of the true image. The restoration process involves Wiener filtering instead of usual inverse filtering in iterative loop. The filter coefficient β that depends on the noise level is also estimated mathematically taking pixel values of blurred image and its median filtered version into consideration. The conventional IBD with these twofold modifications is implemented and experimental results show satisfactory convergence, uniqueness and robustness.