  We investigate the use of Genetic Programming (GP) as a convolutional
predictor for missing pixels in images. The training phase is performed by
sweeping a sliding window over an image, where the pixels on the border
represent the inputs of a GP tree. The output of the tree is taken as the
predicted value for the central pixel. We consider two topologies for the
sliding window, namely the Moore and the Von Neumann neighborhood. The best GP
tree scoring the lowest prediction error over the training set is then used to
predict the pixels in the test set. We experimentally assess our approach
through two experiments. In the first one, we train a GP tree over a subset of
1000 complete images from the MNIST dataset. The results show that GP can learn
the distribution of the pixels with respect to a simple baseline predictor,
with no significant differences observed between the two neighborhoods. In the
second experiment, we train a GP convolutional predictor on two degraded
images, removing around 20% of their pixels. In this case, we observe that the
Moore neighborhood works better, although the Von Neumann neighborhood allows
for a larger training set.
