  Images cannot always be expected to come in a certain standard format and
orientation. Deep networks need to be trained to take into account unexpected
variations in orientation or format. For this purpose, training data should be
enriched to include different conditions. In this study, the effects of data
enrichment on the performance of deep networks in the super resolution problem
were investigated experimentally. A total of six basic image transformations
were used for the enrichment procedures. In the experiments, two deep network
models were trained with variants of the ILSVRC2012 dataset enriched by these
six image transformation processes. Considering a single image transformation,
it has been observed that the data enriched with 180 degree rotation provides
the best results. The most unsuccessful result was obtained when the models
were trained on the enriched data generated by the flip upside down process.
Models scored highest when trained with a mix of all transformations.
