Fourier ptychographic reconstruction with denoising diffusion probabilistic models
文献类型：期刊论文
| 作者 | Wu, Kai1,2; Pan, An2; Gao, Wei2
|
| 刊名 | Optics and Laser Technology
 |
| 出版日期 | 2024-09 |
| 卷号 | 176 |
| 关键词 | Fourier ptychographic microscopy Denoising diffusion probabilistic models Deep learning State matching Gradient descent correction |
| ISSN号 | 00303992 |
| DOI | 10.1016/j.optlastec.2024.111016 |
| 产权排序 | 1 |
| 英文摘要 | 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 |
| 语种 | 英语 |
| 出版者 | Elsevier Ltd |
| 源URL | [http://ir.opt.ac.cn/handle/181661/97431]  |
| 专题 | 西安光学精密机械研究所_瞬态光学技术国家重点实验室 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing; 100049, China 2.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, 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). |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。