To balance the speed and accuracy in semantic segmentation of the urban street images for autonomous driving, we proposed an improved U-Net network. Firstly, to improve the model representation capability, our improved U-Net network structure was designed as three parts, shallow layer, intermediate layer and deep layer. Different attention mechanisms were used according to their feature extraction characteristics. Specifically, a spatial attention module was used in the shallow network, a dual attention module was used in the intermediate layer network and a channel attention module was used in the deep network. At the same time, the traditional convolution was replaced by depthwise separable convolution in above three parts, which can largely reduce the number of network parameters, and improve the network operation speed greatly. The experimental results on three datasets show that our improved U-Net semantic segmentation model for street images can get better results in both segmentation accuracy and speed. The average mean intersection over union (MIoU) is 68.8%, which is increased by 9.2% and the computation speed is about 38 ms/frame. We can process 27 frames images for segmentation per second, which meets the real-time process and accuracy requirements for semantic segmentation of urban street images.

提出一种利用图像翻转和复域编码消除离轴干涉载频影响，进而实现相位恢复的方法。方法通过旋转样品干涉图180°，得到翻转干涉图。样品干涉图与翻转干涉图首先被复数域编码为一幅合成的干涉图。继而进行傅里叶变换，可通过带通滤波器提取频谱中相互分离的共轭项用于相位恢复。通过逆傅里叶变换，可以获得含有样品干涉图和翻转干涉图的相位分布、载频信息的结果。通过除法运算，载频得以在无需复杂运算、解包裹、系统先验信息的情况下被消除。通过仿真和实验验证了算法的可行性。实验结果表明该方法可获得精确的相位恢复结果。在恢复薄相位样品时，该方法的恢复时间仅为原图像翻转方法的23.32%。