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.

翻译：我们调查了利用基因方案(GP)作为图像中缺失像素的聚合预测器。 培训阶段是通过在图像上扫过一个滑动窗口来完成的, 图像上的像素代表了 GP树的输入。 树的输出被作为中像素的预测值。 我们考虑滑动窗口的两个表层, 即摩尔和冯纽曼邻居。 在第二个实验中, 我们用在训练组上得分最低的预测误差的最佳GP树来预测测试组中的像素。 我们通过两个实验来实验评估我们的方法。 在第一个实验中, 我们训练了一棵GP树, 超过MNIST数据集上1000个完整图像的子组。 结果显示, GP可以学习简单的基线预测器的像素分布, 两个相邻之间没有观察到显著的差异。 在第二个实验中, 我们用两种退化的图像来训练GP的象素预测器, 消除了大约20%的像素。 我们观察到, 摩尔邻居的工作效果更好, 尽管 Von Neumann街区允许进行更大规模的训练。