ABSTRACT
This chapter introduces extended neural network algorithms to iteratively perform the restoration. The most common method to compare the similarity of two images is to compute their mean square error (MSE). The MSE relates to the power of the error signal and has little relationship to human visual perception. An important drawback to the MSE and any cost function that attempts to use the MSE to restore a degraded image is that the MSE treats the image as a stationary process. All pixels are given equal priority regardless of their relevance to human perception. When humans observe the differences between two images, they do not give much consideration to the differences in individual pixel-level values. Instead humans are concerned with matching edges, regions, and textures between the two images. This is contrary to the concepts involved in the MSE.
