Abstract: The present disclosure of some embodiments provide a phase reconstruction method for differential interference contrast microscopy based on a pix2pix network, the method comprising the following steps: S1, constructing an end-to-end deep learning strategy based on the pix2pix network; S2, collecting and constructing dataset; S3, Training the pix2pix network; S4, analyzing network accuracy and convergence, and recording error curves of the datasets; S5, analyzing network performance based on error curves of the training set and the test set; S6, performing quantitative phase reconstruction for differential interference contrast microscopy using the trained pix2pix network.

Abstract: The present invention discloses an intelligent phase control method for laser coherent combination for solving the difficulties of high hardware requirements of traditional methods, low robustness of phase control methods based on deep learning. The method of the present invention is to repeatedly input the diffraction patterns after coherent combination of beams with random phases into a reinforcement learning system to update the parameters of the network inside according to the actions it performs and the rewards it obtains. After updating, the diffraction pattern is input to the network, the network outputs some values and the action corresponding to the largest value is selected and converted into a correction signal to the phase controllers, which adjusts the phase of the beam by voltage to obtain a high output. The method of the present invention can efficiently realize the phase control of the laser with high robustness and real-time performance.