Figure 14.

Experiment 1: Convergence and trained models. Learning curves (training/validation) and examples of visual performance (test) of all the models trained in experiment 1. The distortion ${\varepsilon }_{LMS}$-Problem refers to the original degradation of the images (previous to the application of the net). This distortion describes how difficult the compensation problem is. The distortion ${\varepsilon }_{LMS}$-solution refers to the degradation remaining in the signal after the application of the net. It describes how close the output is to the ideal result. Performance numbers have been truncated to the significance of the standard deviation in the test set.