Lensfree computational imaging based on multi-distance phase retrieval

2018 ◽  
Vol 47 (10) ◽  
pp. 1002002
Author(s):  
刘正君 Liu Zhengjun ◽  
郭 澄 Guo Cheng ◽  
谭久彬 Tan Jiubin
Keyword(s):  
Phase Retrieval ◽  
Computational Imaging

scholarly journals A Simply Equipped Fourier Ptychography Platform Based on an Industrial Camera and Telecentric Objective

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4913
Author(s):  
Shaohui Zhang ◽  
Guocheng Zhou ◽  
Ying Wang ◽  
Yao Hu ◽  
Qun Hao
Keyword(s):  
Phase Retrieval ◽  
Dynamic Range ◽  
Numerical Aperture ◽  
Complementary Metal Oxide Semiconductor ◽  
Computational Imaging ◽  
Field Of View ◽  
Oxide Semiconductor ◽  
Imaging Method ◽  
Wide Field ◽  
Wide Field Of View

Fourier ptychography microscopy (FPM) is a recently emerged computational imaging method, which combines the advantages of synthetic aperture and phase retrieval to achieve super-resolution microscopic imaging. FPM can bypass the diffraction limit of the numerical aperture (NA) system and achieve complex images with wide field of view and high resolution (HR) on the basis of the existing microscopic platform, which has low resolution and wide field of view. Conventional FPM platforms are constructed based on basic microscopic platform and a scientific complementary metal–oxide–semiconductor (sCMOS) camera, which has ultrahigh dynamic range. However, sCMOS, or even the microscopic platform, is too expensive to afford for some researchers. Furthermore, the fixed microscopic platform limits the space for function expansion and system modification. In this work, we present a simply equipped FPM platform based on an industrial camera and telecentric objective, which is much cheaper than sCMOS camera and microscopic platform and has accurate optical calibration. A corresponding algorithm was embedded into a conventional FP framework to overcome the low dynamic range of industrial cameras. Simulation and experimental results showed the feasibility and good performance of the designed FPM platform and algorithms.

scholarly journals Phaseless Terahertz Coded-Aperture Imaging for Sparse Target Based on Phase Retrieval Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4617 ◽  
Author(s):  
Peng ◽  
Luo ◽  
Deng ◽  
Wang ◽  
Chen ◽  
...  
Keyword(s):  
Phase Retrieval ◽  
Computational Imaging ◽  
Retrieval Algorithm ◽  
Coded Aperture ◽  
Imaging Method ◽  
Coded Aperture Imaging ◽  
Imaging Model ◽  
Random Frequency ◽  
Resolution Imaging ◽  
Sparse Prior

Phaseless terahertz coded-aperture imaging (PL-TCAI) is a novel radar computational imaging method that utilizes the coded aperture and the incoherent detector array to achieve forward-looking and high-resolution imaging without relying on relative motion. In this paper, we propose a more reasonable and compact architecture for the PL-TCAI system and derive the imaging model of PL-TCAI based on the random frequency-hopping signal. Since most phase retrieval algorithms for PL-TCAI utilize only the intensity of echo signals to accurately reconstruct the target, excessive measurement samples are usually required. In order to reduce the number of measurement samples required for imaging, this paper proposes a sparse Wirtinger flow algorithm with optimal stepsize (SWFOS) by using the sparse prior of the target. The specific procedures of the SWFOS algorithm include the support recovery, initialization by truncated spectral method, iteration via gradient descent scheme, hard threshold operation, and stepsize optimization of iteration. Numerical simulations are performed, and the results show that the SWFOS algorithm not only has good performance for the PR problem, but can also sharply reduce the number of measurement samples required for imaging in the PL-TCAI system.

scholarly journals Large-scale phase retrieval

eLight ◽  
2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Xuyang Chang ◽  
Liheng Bian ◽  
Jun Zhang
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Phase Retrieval ◽  
Complex Space ◽  
Signal To Noise Ratio ◽  
Real Space ◽  
Computational Imaging ◽  
Phase Imaging ◽  
Low Computational Complexity ◽  
Alternating Projection

AbstractHigh-throughput computational imaging requires efficient processing algorithms to retrieve multi-dimensional and multi-scale information. In computational phase imaging, phase retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements. The existing PR algorithms suffer from the tradeoff among low computational complexity, robustness to measurement noise and strong generalization on different modalities. In this work, we report an efficient large-scale phase retrieval technique termed as LPR. It extends the plug-and-play generalized-alternating-projection framework from real space to nonlinear complex space. The alternating projection solver and enhancing neural network are respectively derived to tackle the measurement formation and statistical prior regularization. This framework compensates the shortcomings of each operator, so as to realize high-fidelity phase retrieval with low computational complexity and strong generalization. We applied the technique for a series of computational phase imaging modalities including coherent diffraction imaging, coded diffraction pattern imaging, and Fourier ptychographic microscopy. Extensive simulations and experiments validate that the technique outperforms the existing PR algorithms with as much as 17dB enhancement on signal-to-noise ratio, and more than one order-of-magnitude increased running efficiency. Besides, we for the first time demonstrate ultra-large-scale phase retrieval at the 8K level ($$7680\times 4320$$ 7680 × 4320 pixels) in minute-level time.

Quantitative analysis by reconstruction of HREM focal series

1994 ◽  
Vol 52 ◽  
pp. 740-741
Author(s):  
W. Coene ◽  
A. Thust ◽  
M. Op de Beeck ◽  
D. Van Dyck
Keyword(s):  
Phase Retrieval ◽  
Image Plane ◽  
Initial Guess ◽  
Recursive Least Squares ◽  
Objective Lens ◽  
Electron Wave ◽  
Electron Sources ◽  
Original Algorithm ◽  
Coherent Field ◽  
Direct Interpretation

Compared to conventional electron sources, the use of a highly coherent field-emission gun (FEG) in TEM improves the information resolution considerably. A direct interpretation of this extra information, however, is hampered since amplitude and phase of the electron wave are scrambled in a complicated way upon transfer from the specimen exit plane through the objective lens towards the image plane. In order to make the additional high-resolution information interpretable, a phase retrieval procedure is applied, which yields the aberration-corrected electron wave from a focal series of HRTEM images (Coene et al, 1992).Kirkland (1984) tackled non-linear image reconstruction using a recursive least-squares formalism in which the electron wave is modified stepwise towards the solution which optimally matches the contrast features in the experimental through-focus series. The original algorithm suffers from two major drawbacks : first, the result depends strongly on the quality of the initial guess of the first step, second, the processing time is impractically high.

Limitations to the Spacing Effect

2005 ◽  
Vol 52 (4) ◽  
pp. 257-263 ◽  
Author(s):  
Peter P. J. L. Verkoeijen ◽  
Remy M. J. P. Rikers ◽  
Henk G. Schmidt
Keyword(s):  
Free Recall ◽  
Phase Retrieval ◽  
Incidental Learning ◽  
Factor Model ◽  
Recall Performance ◽  
Spacing Effect ◽  
Word List ◽  
Intentional Learning ◽  
Study Phase ◽  
Learning Condition

Abstract. The spacing effect refers to the finding that memory for repeated items improves when the interrepetition interval increases. To explain the spacing effect in free-recall tasks, a two-factor model has been put forward that combines mechanisms of contextual variability and study-phase retrieval (e.g., Raaijmakers, 2003 ; Verkoeijen, Rikers, & Schmidt, 2004 ). An important, yet untested, implication of this model is that free recall of repetitions should follow an inverted u-shaped relationship with interrepetition spacing. To demonstrate the suggested relationship an experiment was conducted. Participants studied a word list, consisting of items repeated at different interrepetition intervals, either under incidental or under intentional learning instructions. Subsequently, participants received a free-recall test. The results revealed an inverted u-shaped relationship between free recall and interrepetition spacing in both the incidental-learning condition and the intentional-learning condition. Moreover, for intentionally learned repetitions, the maximum free-recall performance was located at a longer interrepetition interval than for incidentally learned repetitions. These findings are interpreted in terms of the two-factor model of spacing effects in free-recall tasks.

Study-Phase Retrieval: Suppressing False Recognition in a Repetition Paradigm

2007 ◽  
Author(s):  
Peter M. Wessels ◽  
Jonathan Schnader ◽  
Allison Smith ◽  
Christopher Thomas ◽  
Haley Titus
Keyword(s):  
False Recognition ◽  
Phase Retrieval ◽  
Study Phase

X-ray microscopy by phase-retrieval methods at the advanced light source

2003 ◽  
Vol 104 ◽  
pp. 557-561 ◽  
Author(s):  
M. R. Howells ◽  
H. Chapman ◽  
S. Hau-Riege ◽  
H. He ◽  
S. Marchesini ◽  
...  
Keyword(s):  
Light Source ◽  
Phase Retrieval ◽  
X Ray ◽  
Advanced Light Source
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