Fourier Ptychographic Microscopy Reconstruction Method Based on Residual Transfer Networks

Fourier ptychographic microscopy reconstruction mostly adopts the traditional alternating iterative phase recovery method and optimization method, which has high computational complexity, high redundancy of image acquisition data, low reconstruction quality and high time consumption. In this paper, the model of residual transfer networks based on Resnet152 is proposed for Fourier ptychographic microscopy reconstruction, the learning process of deep convolution neural network is introduced, and the image reconstruction method based on deep learning realizes the end-to-end reconstruction of low-resolution images to high-resolution images. Through comparative experiments and analysis, the residual network can overcome the gradient explosion, make the feature information more complete and efficient, and the incremental up-sampling reconstruction network has higher image quality, lower computational complexity and shorter running time.