
unet CNN based fourier ptychography
Fourier ptychography is a recently explored imaging method for overcomin...
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PgNN: Physicsguided Neural Network for Fourier Ptychographic Microscopy
Fourier ptychography (FP) is a newly developed computational imaging app...
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DataDriven Design for Fourier Ptychographic Microscopy
Fourier Ptychographic Microscopy (FPM) is a computational imaging method...
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Phase retrieval for Fourier Ptychography under varying amount of measurements
Fourier Ptychography is a recently proposed imaging technique that yield...
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Convolutional neural network for Fourier ptychography video reconstruction: learning temporal dynamics from spatial ensembles
Convolutional neural networks (CNNs) have gained tremendous success in s...
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DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrieval
Fourier phase retrieval is a classical problem of restoring a signal onl...
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Structured illumination microscopy image reconstruction algorithm
Structured illumination microscopy (SIM) is a very important superresol...
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Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow
Fourier ptychography is a recently developed imaging approach for large fieldofview and highresolution microscopy. Here we model the Fourier ptychographic forward imaging process using a convolution neural network (CNN) and recover the complex object information in the network training process. In this approach, the input of the network is the point spread function in the spatial domain or the coherent transfer function in the Fourier domain. The object is treated as 2D learnable weights of a convolution or a multiplication layer. The output of the network is modeled as the loss function we aim to minimize. The batch size of the network corresponds to the number of captured lowresolution images in one forward / backward pass. We use a popular opensource machine learning library, TensorFlow, for setting up the network and conducting the optimization process. We analyze the performance of different learning rates, different solvers, and different batch sizes. It is shown that a large batch size with the Adam optimizer achieves the best performance in general. To accelerate the phase retrieval process, we also discuss a strategy to implement Fouriermagnitude projection using a multiplication neural network model. Since convolution and multiplication are the two mostcommon operations in imaging modeling, the reported approach may provide a new perspective to examine many coherent and incoherent systems. As a demonstration, we discuss the extensions of the reported networks for modeling singlepixel imaging and structured illumination microscopy (SIM). 4frame resolution doubling is demonstrated using a neural network for SIM. We have made our implementation code opensource for the broad research community.
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