Fourier Ptychographic Microscopy (FPM) is an imaging procedure that overcomes the traditional limit on Space-Bandwidth Product (SBP) of conventional microscopes through computational means. It utilizes multiple images captured using a low numerical aperture (NA) objective and enables high-resolution phase imaging through frequency domain stitching. Existing FPM reconstruction methods can be broadly categorized into two approaches: iterative optimization based methods, which are based on the physics of the forward imaging model, and data-driven methods which commonly employ a feed-forward deep learning framework. We propose a hybrid model-driven residual network that combines the knowledge of the forward imaging system with a deep data-driven network. Our proposed architecture, LWGNet, unrolls traditional Wirtinger flow optimization algorithm into a novel neural network design that enhances the gradient images through complex convolutional blocks. Unlike other conventional unrolling techniques, LWGNet uses fewer stages while performing at par or even better than existing traditional and deep learning techniques, particularly, for low-cost and low dynamic range CMOS sensors. This improvement in performance for low-bit depth and low-cost sensors has the potential to bring down the cost of FPM imaging setup significantly. Finally, we show consistently improved performance on our collected real data.

翻译：Friier Ptyphlogy 显微镜(FPM)是一种成像程序,它通过计算手段克服了传统显微镜对空间带宽产品(SBP)的传统限制,它利用了使用低数值孔径(NA)目标捕获的多图像,并通过频域缝合使高分辨率成像。现有的FPM重建方法可以大致分为两种方法:基于前方成像模型物理学的迭代优化法,以及通常采用向前深层学习框架的由数据驱动的方法。我们提议建立一个混合模型驱动的残余网络,将远方成像系统的知识与深数据驱动的网络结合起来。我们提议的建筑、LWGNet、松动传统的Wirtinger流优化算法将新颖的神经网络设计纳入通过复杂的演化区块加强梯度图像。与其他常规的发动技术不同,LWGNet在进行平坦或甚至优于现有传统和深层学习技术的阶段工作时,特别是在低成本和低动态范围CMOS传感器方面。我们所收集的低位深度和低位图像传感器的性改进了低位深度和低成本。我们所收集到的实际成像传感器的性能都显示我们不断改进了实际成像学的潜力。