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-I am a final-year undergraduate student majoring in Optoelectronic Information Science and Engineering at [Sichuan University](https://en.scu.edu.cn/). My research interests include Computational Imaging, 3D Display, and Biomedical Imaging. I originally majored in Architecture in the School of Architecture and Environment at SCU but switched to Optoelectronic Information Science and Engineering in 2021 due to my interest in imaging, display, and holography.
+I am a final-year undergraduate student majoring in Optoelectronic Information Science and Engineering at [Sichuan University](https://en.scu.edu.cn/). My research interests include Computational Imaging, 3D Display, and Biomedical Imaging. I originally majored in Architecture in the School of Architecture and Environment at SCU but switched to Optoelectronic Information Science and Engineering in 2021 due to my interest in optical imaging, display, and holography.
 
 I have had the privilege of being advised by [Prof. An Pan](http://www.piclaboratory.com/) from the Xi'an Institute of Optics and Precision Mechanics, [Chinese Academy of Sciences](https://english.cas.cn/) in the field of **computational imaging** and **Fourier ptychography**. During the period from July 2022 to July 2023, I was advised by [Prof. Jun Wang](https://eie.scu.edu.cn/info/1044/7779.htm) from the College of Electronics and Information Engineering at Sichuan University in the field of **computer-generated holograms** and **diffraction calculations**.
 
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 Fannuo Xu, Zipei Wu, **Chao Tan**, Yizheng Liao, Keru Chen, and An Pan<sup>∗</sup>
 
 
-**Abstract**: In 2013, Fourier ptychographic microscopy (FPM) emerged as a groundbreaking imaging technique, garnering widespread attention due to its remarkable features: high resolution, wide field-of-view (FOV), and quantitative phase recovery. Over the past decade, FPM has evolved into a pivotal tool in microscopy, finding applications in diverse fields such as biomedicine, scientific research, and inspection and metrology. This evolution is rooted in its ability to address the long-standing challenge of balancing resolution and FOV in imaging systems. In this comprehensive review, we delve into the fundamental principles of FPM, drawing comparisons with related imaging modalities. Furthermore, we explore various experimental implementations and highlight key milestones in four crucial technological aspects: speed, three-dimensionality, color imaging, and the integration of deep learning. As a high-throughput optical imaging technique, FPM offers a multitude of applications, ranging from digital pathology and drug screening to label-free imaging. Despite its already impressive accomplishments, we emphasize that FPM is still in its nascent stages, leaving ample room for further advancements. We conclude by discussing the pertinent challenges that lie ahead and the promising future applications of FPM.
+**Abstract**: Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.
 
 ## Fast Scaled Cylindrical Holography Based on Scaled Convolution
 **Chao Tan**, Jun Wang<sup>∗</sup>, Yang Wu, Jie Zhou, and Ni Chen (2022/7 - 2023/7)