Fourier ptychography (FP) is a new computational imaging technique with the advantage of being able to provide super-resolution imaging. FP has a very complex degradation process. Merging with Fourier transforms and pupil aperture scanning causes difficulty in reconstructing high-resolution images by the commonly used deep neural network methods, e.g., based on convolutional neural networks (CNNs). In this paper, we propose a new optimization algorithm for FP, which is carefully designed so that it only constrains concise operations. Then, we unfold the proposed algorithm to design a new neural network, MDFP-Net, specifically for the FP task. MDFP-Net is consistent with a few stages, which well corresponds to the iterations of the proposed optimization algorithm for FP. This not only makes MDFP-Net more intuitively interpretable, but also makes MDFP-Net much more suitable for FP tasks than commonly used CNNs. Moreover, we have built a long-distance reflection FP measurement system and tested our neural network in real experiments. Simulation and real experimental results show that the proposed network can provide better reconstruction results than either traditional algorithms or other deep learning methods. Code is available at https://github.com/BP113/MDFPNET.

Abstract Medical image segmentation remains particularly challenging for complex and low‐contrast anatomical structures, especially in brain MRI glioma segmentation. Gliomas appear with extensive heterogeneity in appearance and location on brain MR images, making robust tumour segmentation extremely challenging and leads to highly variable even in manual segmentation. U‐Net has become the de facto standard in medical image segmentation tasks with great success. Previous researches have proposed various U‐Net‐based 2D Convolutional Neural Networks (2D‐CNN) and their 3D variants, called 3D‐CNN‐based architectures, for capturing contextual information. However, U‐Net often has limitations in explicitly modelling long‐term dependencies due to the inherent locality of convolution operations. Inspired by the recent success of natural language processing transformers in long‐range sequence learning, a multi‐view 2D U‐Nets with transformer (TransMVU) method is proposed, which combines the advantages of transformer and 2D U‐Net. On the one hand, the transformer encodes the tokenized image patches in the CNN feature map into an input sequence for extracting global context for global feature modelling. On the other hand, multi‐view 2D U‐Nets can provide accurate segmentation with fewer parameters than 3D networks. Experimental results on the BraTS20 dataset demonstrate that our model outperforms state‐of‐the‐art 2D models and classic 3D model.