Abstract
Convolutional neural networks rely on image texture and
structure to serve as discriminative features to classify the
image content. Image enhancement techniques can be used
as preprocessing steps to help improve the overall image
quality and in turn improve the overall effectiveness of a
CNN. Existing image enhancement methods, however, are
designed to improve the perceptual quality of an image for a
human observer. In this paper, we are interested in learning
CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classifi-
cation and not necessarily human perception. To this end,
we present a unified CNN architecture that uses a range of
enhancement filters that can enhance image-specific details
via end-to-end dynamic filter learning. We demonstrate the
effectiveness of this strategy on four challenging benchmark
datasets for fine-grained, object, scene, and texture classi-
fication: CUB-200-2011, PASCAL-VOC2007, MIT-Indoor,
and DTD. Experiments using our proposed enhancement
show promising results on all the datasets. In addition, our
approach is capable of improving the performance of all
generic CNN architectures