Phase retrieval using coherent imaging systems with linear transfer functions

2004 ◽  
Vol 234 (1-6) ◽  
pp. 87-105 ◽  
Author(s):  
David Paganin ◽  
Timur E. Gureyev ◽  
Konstantin M. Pavlov ◽  
Robert A. Lewis ◽  
Marcus Kitchen
Keyword(s):  
Phase Retrieval ◽  
Transfer Functions ◽  
Imaging Systems ◽  
Coherent Imaging

scholarly journals Vortex-enhanced coherent-illumination phase diversity for phase retrieval in coherent imaging systems

2016 ◽  
Vol 41 (8) ◽  
pp. 1817 ◽  
Author(s):  
Santiago Echeverri-Chacón ◽  
René Restrepo ◽  
Carlos Cuartas-Vélez ◽  
Néstor Uribe-Patarroyo
Keyword(s):  
Phase Retrieval ◽  
Imaging Systems ◽  
Coherent Imaging ◽  
Phase Diversity ◽  
Coherent Illumination

scholarly journals Sub-Diffraction Visible Imaging Using Macroscopic Fourier Ptychography and Regularization by Denoising

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3154 ◽  
Author(s):  
Zhixin Li ◽  
Desheng Wen ◽  
Zongxi Song ◽  
Gang Liu ◽  
Weikang Zhang ◽  
...  
Keyword(s):  
Phase Retrieval ◽  
State Of The Art ◽  
Imaging Technique ◽  
Speckle Noise ◽  
Laser Speckle ◽  
Coherent Imaging ◽  
Quantitative Metrics ◽  
Visible Imaging ◽  
Flow Algorithm ◽  
Diffraction Imaging

Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications.

scholarly journals Deep learning in holography and coherent imaging

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Yair Rivenson ◽  
Yichen Wu ◽  
Aydogan Ozcan
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Imaging Systems ◽  
New Paradigm ◽  
Coherent Imaging ◽  
Holographic Image ◽  
Engineering Research ◽  
Recent Advances ◽  
Reconstruction Methods ◽  
Recovery Techniques

Abstract Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography. These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications.

scholarly journals Phase retrieval for arbitrary Fresnel-like linear shift-invariant imaging systems suitable for tomography

2018 ◽  
Vol 9 (9) ◽  
pp. 4390 ◽  
Author(s):  
Stanislav Hrivňak ◽  
Andrej Hovan ◽  
Jozef Uličný ◽  
Patrik Vagovič
Keyword(s):  
Phase Retrieval ◽  
Imaging Systems

Methods of adaptive optics in coherent imaging systems

1996 ◽  
Author(s):  
Anatoly A. Kornienko ◽  
Genady N. Maltsev ◽  
M. Y. Denisenko
Keyword(s):  
Adaptive Optics ◽  
Imaging Systems ◽  
Coherent Imaging
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