Neural network identification and restoration of blurred images
В. Н. Карнаухов, Igor N. Aizenberg, Constantine Butakoff, В. Н. Карнаухов, Nikolay S. Merzlyakov, Olga Milukova, Yu‐Jin Zhang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
There are different techniques available for solving of the restoration problem including Fourier domain techniques, regularization methods, recursive and iterative filters to name a few. But without knowing at least approximate parameters of the blur, these methods often show poor results. If incorrect blur model is chosen then the image will be rather distorted much more than restored. The original solution of the blur and blur parameters identification problem is presented in this paper. A neural network based on multi-valued neurons is used for the blur and blur parameters identification. It is shown that it is possible to identify the type of the distorting operator by using simple single-layered neural network. Four types of blur operators are considered: defocus, rectangular, motion, and Gaussian ones. The parameters of the corresponding operator are identified by using a similar neural network. After identification of the blur type and its parameters the image can be restored using different methods. Some fundamentals of image restoration techniques are also considered.