Neural network model with positional deviation correction for Fourier ptychography,Journal of the Society for Information Display

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Neural network model with positional deviation correction for Fourier ptychography
Journal of the Society for Information Display ( IF 1.7 ) Pub Date : 2021-05-10 , DOI: 10.1002/jsid.1030
Ming Zhao ₁ , Xiaohui Zhang ₁ , Zhiming Tian ₁ , Shuai Liu ₁

Affiliation

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.

中文翻译：

用于傅里叶 ptychography 的具有位置偏差校正的神经网络模型

Fourier ptychography 是一种新型的计算成像技术，它使用从重叠孔径（或等效物）获得的一堆低分辨率图像来重建超分辨率图像。然而，孔径位置的精度将直接影响重建图像的质量和分辨率。本文提出了使用神经网络进行 FP 位置偏差校正的新视角。我们构建了一个可训练的神经网络来执行位置偏差校正和对象重建。将物体的实部和虚部以及每个孔径的不同和不规则位置偏差设置为卷积层的权重。这些权重的梯度是自动计算的，并采用基于梯度的优化算法来恢复目标并找到正确的光圈位置。进行仿真和实验以验证我们的算法。结果表明，所提算法能够准确地找到孔径位置，提高了重建质量。

更新日期：2021-05-10

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