  Dense pixelwise prediction such as semantic segmentation is an up-to-date
challenge for deep convolutional neural networks (CNNs). Many state-of-the-art
approaches either tackle the loss of high-resolution information due to pooling
in the encoder stage, or use dilated convolutions or high-resolution lanes to
maintain detailed feature maps and predictions. Motivated by the structural
analogy between multi-resolution wavelet analysis and the pooling/unpooling
layers of CNNs, we introduce discrete wavelet transform (DWT) into the CNN
encoder-decoder architecture and propose WCNN. The high-frequency wavelet
coefficients are computed at encoder, which are later used at the decoder to
unpooled jointly with coarse-resolution feature maps through the inverse DWT.
The DWT/iDWT is further used to develop two wavelet pyramids to capture the
global context, where the multi-resolution DWT is applied to successively
reduce the spatial resolution and increase the receptive field. Experiment with
the Cityscape dataset, the proposed WCNNs are computationally efficient and
yield improvements the accuracy for high-resolution dense pixelwise prediction.
