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

Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks