论文标题
WAVESNET：小波集成的深层网络用于图像分割
WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation
论文作者
论文摘要
在深网中，丢失的数据详细信息会大大降低图像分割的性能。在本文中，我们建议应用离散小波变换（DWT），以在功能映射下采样过程中提取数据详细信息，并在上采样过程中采用逆DWT（IDWT），并提取了详细信息以恢复详细信息。我们首先将DWT/IDWT转换为通用网络层，该层适用于1D/2D/3D数据以及HAAR，COHEN和DAUBECHIES等各种小波等。然后，我们设计了基于各种架构的图像分段（WAVESNET）的小波集成网络，这些网络基于各种架构，包括U-Net，Segnet，Segnet，Segnet，segnet和deeplabv3+。由于DWT/IDWT在处理数据详细信息方面的有效性，因此对Camvid，Pascal VOC和CityScapes的实验结果表明，我们的WaveSnets具有比其香草版本更好的细分性能。
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.
