Fig. 1. Flowchart of the proposed method where two convolutional networks (CNN1 and CNN2) and the arctangent function are used together to determine the phase distribution. For CNN1 (in red), the input is the fringe image
Fig. 2. Schematic of CNN1, which is composed of convolutional layers and several residual blocks.
Fig. 3. Schematic of CNN2, which is more sophisticated than CNN1 and further includes two pooling layers, an upsampling layer, a concatenation block, and a linearly activated convolutional layer.
Fig. 4. Testing using the trained networks on a scene that is not present in the training phase. (a) Input fringe image
Fig. 5. Comparison of the phase error of different methods: (a) FT, (b) WFT, (c) our method, and (d) magnified views of the phase error for two selected complex regions.
Fig. 6. Comparison of the 3-D reconstruction results for different methods: (a) FT, (b) WFT, (c) our method, and (d) ground truth obtained by the 12-step PS profilometry.
Please Enter Your Email: