Blind deep-learning based preprocessing method for Fourier ptychographic microscopy | |
Wu, Kai1,2; Pan, An1; Sun, Zhonghan1; Shi, Yinxia1,2; Gao, Wei1![]() | |
作者部门 | 瞬态光学研究室 |
2024-02 | |
发表期刊 | Optics and Laser Technology
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ISSN | 00303992 |
卷号 | 169 |
产权排序 | 1 |
摘要 | Fourier ptychographic microscopy (FPM) is a technique for tackling the trade-off between the resolution and the imaging field of view by combining the techniques from aperture synthesis and phase retrieval to estimate the complex object from a series of low-resolution intensity images captured under angle-varied illumination. The captured images are commonly corrupted by multiple noise, leading to the degradation of the reconstructed image quality. Typically speaking, the noise model and noise level of the experimental images are unknown, and the traditional image denoising methods have limited effect. In this paper we model the FPM forward imaging process corrupted by noise and divide the noise in the captured images into two parts: the signal-dependent part and the signal-independent part. Based on the noise model we propose a novel blind deep-learning based Fourier ptychographic microscopy preprocessing method, termed BDFP, for removing these two components of noise. First, from a portion of the captured low-resolution images, a set of blocks corresponding to the smooth area of the object are extracted to model signal-independent noise. Second, under the assumption that the signal-dependent noise follows a Poisson distribution, we add Poisson noise and signal-independent noise blocks to clean images to form a paired training dataset, which is then used for training a deep convolutional neural network (CNN) model to reduce both signal-dependent noise and signal-independent noise. The proposed blind preprocessing method, combining with typical FPM reconstruction algorithms, is tested on simulated data and experimental images. Experimental results show that our preprocessing method can significantly reduce the noise in the captured images and bring about effective improvements in reconstructed image quality. © 2023 Elsevier Ltd |
关键词 | Fourier ptychographic microscopy Noise modeling Blind denoising Deep learning |
DOI | 10.1016/j.optlastec.2023.110140 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:001159160100001 |
出版者 | Elsevier Ltd |
EI入藏号 | 20234014830596 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/96824 |
专题 | 瞬态光学研究室 |
通讯作者 | Pan, An |
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,Sun, Zhonghan,et al. Blind deep-learning based preprocessing method for Fourier ptychographic microscopy[J]. Optics and Laser Technology,2024,169. |
APA | Wu, Kai,Pan, An,Sun, Zhonghan,Shi, Yinxia,&Gao, Wei.(2024).Blind deep-learning based preprocessing method for Fourier ptychographic microscopy.Optics and Laser Technology,169. |
MLA | Wu, Kai,et al."Blind deep-learning based preprocessing method for Fourier ptychographic microscopy".Optics and Laser Technology 169(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 | ||
Blind deep-learning (10182KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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