  Convolutional neural networks are able to perform a hierarchical learning
process starting with local features. However, a limited attention is paid to
enhancing such elementary level features like edges. We propose and evaluate
two wavelet-based edge feature enhancement methods to preprocess the input
images to convolutional neural networks. The first method develops feature
enhanced representations by decomposing the input images using wavelet
transform and limited reconstructing subsequently. The second method develops
such feature enhanced inputs to the network using local modulus maxima of
wavelet coefficients. For each method, we have developed a new preprocessing
layer by implementing each purposed method and have appended to the network
architecture. Our empirical evaluations demonstrate that the proposed methods
are outperforming the baselines and previously published work with significant
accuracy gains.
