A recursive approach to joint image restoration and compensated blur identification

Presents a new recursive scheme to blind image deconvolution based on joint image restoration and compensated blur identification. The technique projects a novel cost function into the image and blur subspaces, and optimizes them recursively using alternating minimization. A hierarchical neural network is employed to provide an adaptive, perception-based restoration. The sparse connections of the network are instrumental in reducing the computational cost of the restoration. On the other hand, conjugate gradient optimization is adopted to identify the blur due to its computational efficiency. A compensation scheme is developed to address the issue of ambiguous blur identification arising from the edge and hairy texture regions. Experimental results show that the new approach is effective and robust in restoring the degraded images as well as in identifying the blurs.