  Neural networks are becoming central in several areas of computer vision and
image processing and different architectures have been proposed to solve
specific problems. The impact of the loss layer of neural networks, however,
has not received much attention in the context of image processing: the default
and virtually only choice is L2. In this paper, we bring attention to
alternative choices for image restoration. In particular, we show the
importance of perceptually-motivated losses when the resulting image is to be
evaluated by a human observer. We compare the performance of several losses,
and propose a novel, differentiable error function. We show that the quality of
the results improves significantly with better loss functions, even when the
network architecture is left unchanged.
