Fourier ptychographic reconstruction with denoising diffusion probabilistic models | |
Wu, Kai1,2; Pan, An1; Gao, Wei1![]() | |
作者部门 | 瞬态光学研究室 |
2024-09 | |
发表期刊 | Optics and Laser Technology
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ISSN | 00303992 |
卷号 | 176 |
产权排序 | 1 |
摘要 | Fourier ptychographic microscopy (FPM) is a promising computational imaging technique that can bypass the diffraction limit of the objective lens and achieve high-resolution, wide field-of-view imaging. The FPM setups firstly capture a series of low-resolution (LR) intensity images by angle-varied illumination and then reconstruction algorithms recover the high-resolution (HR) complex-valued object from the LR measurements. The image acquisition process commonly introduces noise, ultimately leading to degradation in the quality of the reconstruction results. In this paper, we report a noise-robust Fourier ptychographic reconstruction method that generates the HR complex-valued object estimation using the image priors specified by denoising diffusion probabilistic models (DDPM). First, the initial estimation of the HR complex-valued object is matched with an intermediate state in the Markov chain defined by DDPM. Then, the noisy initial solution is iteratively updated to a high-quality reconstruction result in the reverse process of DDPM and gradient descent correction is incorporated to enforce data consistency with the LR measurements. The proposed method integrates DDPM specified image priors and gradient descent correction, achieving solutions with less noise-related artifacts and high fidelity for HR complex-valued object estimation in Fourier ptychographic reconstruction. We apply the proposed method on both synthetic and real captured data. The experimental results show that our method can efficiently suppress the impact of noise and improve reconstruction results quality. © 2024 Elsevier Ltd |
关键词 | Fourier ptychographic microscopy Denoising diffusion probabilistic models Deep learning State matching Gradient descent correction |
DOI | 10.1016/j.optlastec.2024.111016 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:10.1016/j.optlastec.2024.111016 |
出版者 | Elsevier Ltd |
EI入藏号 | 20241715963426 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/97431 |
专题 | 瞬态光学研究室 |
作者单位 | 1.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; 2.University of Chinese Academy of Sciences, Beijing; 100049, China |
推荐引用方式 GB/T 7714 | Wu, Kai,Pan, An,Gao, Wei. Fourier ptychographic reconstruction with denoising diffusion probabilistic models[J]. Optics and Laser Technology,2024,176. |
APA | Wu, Kai,Pan, An,&Gao, Wei.(2024).Fourier ptychographic reconstruction with denoising diffusion probabilistic models.Optics and Laser Technology,176. |
MLA | Wu, Kai,et al."Fourier ptychographic reconstruction with denoising diffusion probabilistic models".Optics and Laser Technology 176(2024). |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Fourier ptychographi(5721KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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