A recursive soft-decision PSF and neural network approach to adaptive blind image regularization

We present a new approach to adaptive blind image regularization based on a neural network and soft-decision blur identification. We formulate blind image deconvolution into a recursive scheme by projecting and optimizing a novel cost function with respect to its image and blur subspaces. The new algorithm provides a continual blur adaptation towards the best-fit parametric structure throughout the restoration. It integrates the knowledge of real-life blur structures without compromising its flexibility in restoring images degraded by other nonstandard blurs. A nested neural network, called the hierarchical cluster model is employed to provide an adaptive, perception-based restoration. On the other hand, conjugate gradient optimization is adopted to identify the blur. Experimental results show that the new approach is effective in restoring the degraded image without the prior knowledge of the blur.