论文标题
U-NET CNN的傅立叶PTYChography
u-net CNN based fourier ptychography
论文作者
论文摘要
傅立叶Ptychography是一种最近探索的成像方法，用于克服传统摄像机的衍射极限，并在显微镜中应用并产生高分辨率图像。为了将相干光源不同的照明角度下拍摄的低分辨率图像拼接在一起，采用了迭代相检索算法。但是，重建过程很慢，对于连续记录的低分辨率图像，在傅立叶域中需要大量重叠，并且在系统畸变（例如噪声或随机更新序列）下也更糟。在本文中，我们提出了一种基于卷积神经网络的新检索算法。一旦训练有素，我们的模型就可以使用图形处理单元迅速执行高质量的重建。实验表明，我们的模型可以实现更好的重建结果，并且在系统畸变下更健壮。
Fourier ptychography is a recently explored imaging method for overcoming the diffraction limit of conventional cameras with applications in microscopy and yielding high-resolution images. In order to splice together low-resolution images taken under different illumination angles of coherent light source, an iterative phase retrieval algorithm is adopted. However, the reconstruction procedure is slow and needs a good many of overlap in the Fourier domain for the continuous recorded low-resolution images and is also worse under system aberrations such as noise or random update sequence. In this paper, we propose a new retrieval algorithm that is based on convolutional neural networks. Once well trained, our model can perform high-quality reconstruction rapidly by using the graphics processing unit. The experiments demonstrate that our model achieves better reconstruction results and is more robust under system aberrations.
