Efficient approximation of neural filters for removing quantum noise from images

In this paper, efficient filters are presented that approximate neural filters (NFs) that are trained to remove quantum noise from images. A novel analysis method is proposed for making clear the characteristics of the trained NF. In the proposed analysis method, an unknown nonlinear deterministic system with plural inputs such as the trained NIT can be analyzed by using its outputs when the specific input signals are input to it. The experiments on the NFs trained to remove quantum noise from medical and natural images were performed. The results have demonstrated that the approximate filters, which are realized by using the results of the analysis, are sufficient for approximation of the trained NFs and efficient at computational cost.