Going to create a neural network for image reconstruction given above and the like.
Each image is damaged color planes R/G/B on a regular basis (see the callout at the right side of the picture). I.e. as an example — in every fourth (second) pixel leaving only the R-coordinate. Also damaged so the picture is optional, will be present noise (the training will take place at real photos). The amount of damage — about 2/3, i.e., every pixel remains intact only one of the R/G/B values. The output network must give the restored value of the center pixel of a block of NxN, N is naturally an odd number, the maximum block size of 15x15.
The input will be clean by subtracting interpolated on the original image data (as a preliminary result).
While stopped on a multilayer Ann and the network of Elman
If someone faced with the decision of problems of this kind give me some advice on what type of neural network is better to use and how to teach? Whether to use genetic algorithms?