Neural image reconstruction using a heuristic validation mechanism.

Image reconstruction is a mathematical process, where the image is compressed into a small representation and derived from this form. The general use of the reconstruction technique finds a place in noise removal from images obtained in medicine or other areas of life. In this paper, we propose a heuristic validation mechanism for training different types of neural networks in the problem of image reconstruction. The main idea is based on finding some important areas on image by heuristic algorithm and train network until a certain level of entropy of these areas is achieved. The mathematical model of this technique is described and supported by experimental results on different datasets with complex analysis of different heuristics. Proposed approach shows that it can reduce the average time of training process using convolutional neural networks. [ABSTRACT FROM AUTHOR]