  A foveated image can be entirely reconstructed from a sparse set of samples
distributed according to the retinal sensitivity of the human visual system,
which rapidly decreases with increasing eccentricity. The use of Generative
Adversarial Networks has recently been shown to be a promising solution for
such a task, as they can successfully hallucinate missing image information. As
in the case of other supervised learning approaches, the definition of the loss
function and the training strategy heavily influence the quality of the output.
In this work,we consider the problem of efficiently guiding the training of
foveated reconstruction techniques such that they are more aware of the
capabilities and limitations of the human visual system, and thus can
reconstruct visually important image features. Our primary goal is to make the
training procedure less sensitive to distortions that humans cannot detect and
focus on penalizing perceptually important artifacts. Given the nature of
GAN-based solutions, we focus on the sensitivity of human vision to
hallucination in case of input samples with different densities. We propose
psychophysical experiments, a dataset, and a procedure for training foveated
image reconstruction. The proposed strategy renders the generator network
flexible by penalizing only perceptually important deviations in the output. As
a result, the method emphasized the recovery of perceptually important image
features. We evaluated our strategy and compared it with alternative solutions
by using a newly trained objective metric, a recent foveated video quality
metric, and user experiments. Our evaluations revealed significant improvements
in the perceived image reconstruction quality compared with the standard
GAN-based training approach.
