Task Specific Image Enhancement for Improving the Accuracy of CNNs

Choosing an appropriate pre-processing and image enhancement step for CNNs can have a positive effect on the performance. Pre-processing and image enhancement are in contrast to augmentation deterministically applied on every image of a data set and can be interpreted as a normalizing way to construct invariant features. In this paper we present a method that determines the optimal composition and strength of various image enhancement methods by a neural network with a new type of layer that learns the parameters of optimal image enhancement. We apply this procedure on different image classification data sets, which leads to an improvement of the information content of the images with respect to the specific task and thus also to an improvement of the resulting test accuracy. For example, we can reduce the classification error for our benchmark data sets clearly.