  In deep networks, the lost data details significantly degrade the
performances of image segmentation. In this paper, we propose to apply Discrete
Wavelet Transform (DWT) to extract the data details during feature map
down-sampling, and adopt Inverse DWT (IDWT) with the extracted details during
the up-sampling to recover the details. We firstly transform DWT/IDWT as
general network layers, which are applicable to 1D/2D/3D data and various
wavelets like Haar, Cohen, and Daubechies, etc. Then, we design wavelet
integrated deep networks for image segmentation (WaveSNets) based on various
architectures, including U-Net, SegNet, and DeepLabv3+. Due to the
effectiveness of the DWT/IDWT in processing data details, experimental results
on CamVid, Pascal VOC, and Cityscapes show that our WaveSNets achieve better
segmentation performances than their vanilla versions.
