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28 May 2004 Blind image restoration based on RBF neural networks
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Proceedings Volume 5298, Image Processing: Algorithms and Systems III; (2004) https://doi.org/10.1117/12.524688
Event: Electronic Imaging 2004, 2004, San Jose, California, United States
Abstract
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.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ping Guo and Lei Xing "Blind image restoration based on RBF neural networks", Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); https://doi.org/10.1117/12.524688
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