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This paper presents a new approach to blind image deconvolution based on soft-decision blur identification and hierarchical neural networks. Traditional blind algorithms require a hard-decision on whether the blur satisfies a parametric form before their formulations. As the blurring function is usually unknown a priori, this precondition inhibits the incorporation of parametric blur knowledge domain into the restoration schemes. The new technique addresses this difficulty by providing a continual soft-decision blur adaptation with respect to the best-fit parametric structure throughout deconvolution. The approach integrates the knowledge of well-known blur models without compromising its flexibility in restoring images degraded by nonstandard blurs. An optimization scheme is developed where a new cost function is projected and minimized with respect to the image and blur domains. A nested neural network, called the hierarchical cluster model is employed to provide an adaptive, perception-based restoration. Its sparse synaptic connections are instrumental in reducing the computational cost of restoration. Conjugate gradient optimization is adopted to identify the blur due to its computational efficiency. The approach is shown experimentally to be effective in restoring images degraded by different blurs.