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

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

Imaging Systems: Sampling, Demodulation and Phase Retrieval

2002 ◽  
Vol 13 (12) ◽  
pp. 19
Author(s):  
Kedar Khare ◽  
Nicholas George
Keyword(s):  
Phase Retrieval ◽  
Imaging Systems

scholarly journals Blind phase retrieval for aberrated linear shift-invariant imaging systems

2010 ◽  
Vol 12 (7) ◽  
pp. 073040 ◽  
Author(s):  
Rotha P Yu ◽  
David M Paganin
Keyword(s):  
Phase Retrieval ◽  
Imaging Systems

Improved phase retrieval algorithm for optical imaging systems

2013 ◽  
Author(s):  
Shun Qin ◽  
Xinqi Hu ◽  
Bing Dong ◽  
Hongming Zhao ◽  
Huijie Du ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Phase Retrieval ◽  
Imaging Systems ◽  
Retrieval Algorithm ◽  
Phase Retrieval Algorithm

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

A quantitative phase retrieval simulation for mesh-based x-ray phase imaging systems

2021 ◽  
Author(s):  
Laila Hassan ◽  
Weiyuan Sun ◽  
Carolyn A. MacDonald ◽  
Jonathan C. Petruccelli
Keyword(s):  
Phase Retrieval ◽  
Imaging Systems ◽  
Phase Imaging ◽  
X Ray ◽  
Quantitative Phase

scholarly journals Wide-vergence, multi-spectral adaptive optics scanning laser ophthalmoscope with diffraction-limited illumination and collection

2020 ◽  
Author(s):  
Sanam Mozaffari ◽  
Francesco Larocca ◽  
Volker Jaedicke ◽  
Pavan Tiruveedhula ◽  
Austin Roorda
Keyword(s):  
Adaptive Optics ◽  
Phase Retrieval ◽  
Collection Efficiency ◽  
Scanning Laser Ophthalmoscope ◽  
Imaging Systems ◽  
Scanning Laser ◽  
Wavefront Sensing ◽  
Imaging Results ◽  
Imaging Performance ◽  
Spectral Channels

AbstractVisualizing and assessing the function of microscopic retinal structures in the human eye is a challenging task that has been greatly facilitated by ophthalmic adaptive optics (AO). Yet, as AO imaging systems advance in functionality by employing multiple spectral channels and larger vergence ranges, achieving optimal resolution and signal-to-noise ratios (SNR) becomes difficult and is often compromised. While current-generation AO retinal imaging systems have demonstrated excellent, near diffraction-limited imaging performance over wide vergence and spectral ranges, a full theoretical and experimental analysis of an AOSLO that includes both the light delivery and collection optics has not been done, and neither has the effects of extending wavefront correction from one wavelength to imaging performance in different spectral channels. Here, we report a methodology and system design for simultaneously achieving diffraction-limited performance in both the illumination and collection paths for a wide-vergence, multi-spectral AO scanning laser ophthalmoscope (SLO) over a 1.2 diopter vergence range while correcting the wavefront in a separate wavelength. To validate the design, an AOSLO was constructed to have three imaging channels spanning different wavelength ranges (543 ± 11 nm, 680 ± 11 nm, and 840 ± 6 nm, respectively) and one near-infrared wavefront sensing channel (940 ± 5 nm). The AOSLO optics and their alignment were determined via simulations in optical and optomechanical design software and then experimentally verified by measuring the AOSLO’s illumination and collection point spread functions (PSF) for each channel using a phase retrieval technique. The collection efficiency was then measured for each channel as a function of confocal pinhole size when imaging a model eye achieving near-theoretical performance. Imaging results from healthy human adult volunteers demonstrate the system’s ability to resolve the foveal cone mosaic in all three imaging channels despite a wide spectral separation between the wavefront sensing and imaging channels.OCIS codes(110.1080) Active or adaptive optics; (170.4460) Ophthalmic optics and devices; (170.4470) Ophthalmology

Digital imaging and quantitative image analysis of polymer blends

1996 ◽  
Vol 54 ◽  
pp. 170-171
Author(s):  
Xiao Zhang
Keyword(s):  
Digital Imaging ◽  
Imaging Techniques ◽  
Imaging Systems ◽  
Polymer Science ◽  
Key Factors ◽  
Multiple Imaging ◽  
Quantitative Image ◽  
Digital Imaging Systems ◽  
Multi Phase ◽  
Network Technologies

Polymer microscopy involves multiple imaging techniques. Speed, simplicity, and productivity are key factors in running an industrial polymer microscopy lab. In polymer science, the morphology of a multi-phase blend is often the link between process and properties. The extent to which the researcher can quantify the morphology determines the strength of the link. To aid the polymer microscopist in these tasks, digital imaging systems are becoming more prevalent. Advances in computers, digital imaging hardware and software, and network technologies have made it possible to implement digital imaging systems in industrial microscopy labs.

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