1. A method of training an artificial neural network including network parameters that govern how the artificial neural network operates, the method comprising: receiving at least a likelihood distribution map as a teacher image; receiving at least a training image; moving a local window across plural sub-regions of the training image to obtain respective sub-region pixel sets; inputting the sub-region pixel sets to the artificial neural network so that the artificial neural network provides output pixel values; comparing the output pixel values to corresponding teacher image pixel values to determine an error; and training the network parameters of the artificial neural network to reduce the error.