Image restoration based on hierarchical cluster model with evolutionary optimization

In this paper, a new approach to adaptive image regularization based on Hierarchical Cluster Model (HCM) with evolutionary optimization is proposed. HCM is a hierarchical neural network with distributed clusters. Its sparse synaptic connections and parallel structure reduce the computational cost of restoration. Adaptive restoration is achieved by assigning entries of an optimized regularization vector to each homogeneous cluster of the image. The clusters are restored in the order of smooth to texture and edge regions to minimize the regularization error. An evolutionary scheme is employed to improve the performance profile of the restored image and optimize the regularization vector. Experimental results show that the new approach is superior in suppressing noise and ringing at the smooth background while preserving fine details at the texture and edge regions effectively