1 research outputs found
CoInGP: Convolutional Inpainting with Genetic Programming
We investigate the use of Genetic Programming (GP) as a convolutional
predictor for supervised learning tasks in signal processing, focusing on the
use case of predicting missing pixels in images. The training is performed by
sweeping a small sliding window on the available pixels: all pixels in the
window except for the central one are fed in input to a GP tree whose output is
taken as the predicted value for the central pixel. The best GP tree in the
population scoring the lowest prediction error over all available pixels in the
population is then tested on the actual missing pixels of the degraded image.
We experimentally assess this approach by training over four target images,
removing up to 20\% of the pixels for the testing phase. The results indicate
that our method can learn to some extent the distribution of missing pixels in
an image and that GP with Moore neighborhood works better than the Von Neumann
neighborhood, although the latter allows for a larger training set size.Comment: 14 pages, 6 figure