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.

翻译：图像并不总是以特定的标准格式和方向出现。深度网络需要经过训练，以考虑方向或格式的意外变化。为此，训练数据应进行增强以包含不同条件。本研究通过实验探究了数据增强对超分辨率问题中深度网络性能的影响。增强过程共使用了六种基本图像变换。实验中，两个深度网络模型使用经过这六种图像变换过程增强的ILSVRC2012数据集变体进行训练。从单一图像变换来看，观察到180度旋转后的数据增强效果最佳。而使用上下翻转生成增强数据训练模型时，效果最差。当使用所有变换的混合数据进行训练时，模型得分最高。