傅里叶叠层显微成像(FPM)是一种能够重建宽视场和高分辨率图像的新型成像技术。传统的FPM重建算法计算成本高,重建高质量的图像需要较大的图像采集量,这些缺点使得传统重建算法的成像性能和效率较低。因此,提出一种基于深度学习的傅里叶叠层显微成像的神经网络模型,对图像进行低分辨率到高分辨率的端到端映射,有效提高成像性能和效率。首先,借助菱形采样方法进行图像采集,加速低分辨图片采集过程。其次,结合残差结构、密集连接以及通道注意力机制等模块,拓展网络深度、挖掘有用特征,增强网络模型的表达能力和泛化能力。然后,使用子像素卷积进行高效地上采样,恢复高清图像。最后,采用主观和客观的评价方法对重建结果进行评估。结果显示,本文提出的网络模型对比传统重建算法重构效果更优,且降低了计算复杂度,平均重建时间更短。同时,在保证图像重建效果不变的情况下,低分辨率图像的采集数量比传统算法减少了约一半。

Fourier ptychographic microscopy (FPM) is a newly developed imaging technology, which is capable of reconstructing images with a wide field of view and high resolution. However, the reconstruction based on traditional reconstruction algorithms has high calculation cost, large amount of image acquisition, and low efficiency. Therefore, we propose a deep learning-based neural network model of FPM that performs end-to-end mapping from low-resolution to high-resolution to effectively improve imaging performance and efficiency. First, the diamond sampling method is used to speed up the process of image acquisition. Second, the combination of residual structure, dense connection, and channel attention mechanism is used to expand the network depth, mine useful features, and enhance the expression and generalization ability of the network model. Then, sub-pixel convolution is used for efficient upsampling and restoring high-resolution images. Finally, subjective and objective evaluation methods are used to evaluate the reconstruction results. The results show that, compared with the traditional reconstruction algorithm, the proposed network model has better reconstruction effect, lower computational complexity, and shorter average reconstruction time. At the same time, the number of low-resolution images is reduced by about half compared with the traditional algorithm.