Neural Network Model With Positional Deviation Correction For Fourier Ptychography

Fourier ptychography is a new type of computational imaging technology, which uses a stack of low-resolution images obtained from overlapped apertures (or equivalent) to reconstruct super-resolved image. However, the accuracy of the aperture position will directly affect the quality and resolution of the reconstructed image. This paper proposes a new perspective for FP positional deviations correction using the neural network. We construct a trainable neural network to perform positional deviations correction along with object reconstruction. The real part and imaginary part of the object as well as the different and irregular positional deviations of each aperture are set as the weights of convolutional layer. The gradients over these weights are computed automatically, and gradient-based optimization algorithm is employed to recover the object and find the correct aperture position. Simulation and experiment are performed to verify our algorithm. The results show that the proposed algorithm can accurately find the aperture position and improve the reconstruction quality.