2020/03/16 by Yican Chen, Zhi Luo, Chen, Yican +7
Physics and Astronomy · #Advanced X-ray Imaging Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Laser-Plasma Interactions and Diagnostics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Nuclear Physics and Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2003.07460
openalex publication_date 2020/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.