Blind image restoration based on RBF neural networks
Ping Guo, Lei Xing · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
In this paper, we propose a novel technique for blind image restoration and resolution enhancement based on radial basis function (RBF) neural network. The RBF network gives a solution of the regularization problem often seen in function estimation with certain standard smoothness functional used as stabilizers. A RBF network model is designed to represent the observed image. In this model, the number and distribution of the centers (which are set to the pixels of the observed image) are fixed. In addition, network output is set to the observed image pixel gray scale value. The RBF plays a role of point spread function. The technique can also be applied to image resolution enhancement by generating an interpolated image from the low resolution version. Experimental results show that the learning algorithm can effectively estimate the model parameters and the established neural network model has a high fidelity in representing an image. It is believed that the proposed neural network model provides a valuable tool for image restoration and resolution enhancement and holds promises to improve the quality and efficiency of image processing.