Bahram Javidi’s research interests are in transformative approaches to optical imaging sciences. He has been named Fellow of eight scientific societies, including IEEE, American Institute for Medical and Biological Engineering, Optical Society (OSA), European Optical Society, and SPIE. In 2008, he received the Fellow award by John Simon Guggenheim Foundation. He is recognized by eight best paper awards from IEEE, OSA, EOS, and SPIE. He received the 2008 IEEE Donald G. Fink Prize Paper Award chosen among all (over 140) IEEE Transactions, Journals, and Magazines. In 2010, he received George Washington University's Distinguished Alumni Scholar Award, University’s highest honor for its alumni in all disciplines. In 2007, The Alexander von Humboldt Foundation awarded him the Humboldt Prize for outstanding US scientists, Germany's highest research award for senior U.S. scientists and scholars in all disciplines. He received the SPIE’s 2008 Technology Achievement Award and the SPIE’s Dennis Gabor Award in Diffractive Wave Technologies in 2005. Dr. Javidi was the recipient of the IEEE Photonics Society Distinguished Lecturer Award in 2003 and 2004. Early in his career, the National Science Foundation named Prof. Javidi a Presidential Young Investigator. He also received The Engineering Foundation and the IEEE Faculty Initiation Award. Dr. Javidi was selected in 2003 as one of the nation's top 160 engineers between the ages of 30-45 by the National Academy of Engineering to be an invited speaker at The Frontiers of Engineering Conference. He is an alumnus of the Frontiers of Engineering of The National Academy of Engineering since 2003. Prof. Javidi has over 660 publications [9 books, 44 book chapters, 310 peer reviewed journal articles, 370 conference proceedings, 120 Plenary, Keynotes, and invited conference papers]. His papers have been cited 7300 times according to the citation index of WEB of Science (h index=46).
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By using the Liquid Crystal devices, dynamic integral imaging have been successfully applied on 3D Display, capturing, and bio-imaging applications.
Two-step integral imaging for orthoscopic three-dimensional imaging with improved viewing resolution
The use of lp-norm to measure the size of the filter output due to noise gives a greater freedom in adjusting the noise robustness and discrimination capabilities. The flexibility in allowing more general type of constraints allows for experimenting and may lead to designing of filters to obtain better performance by selecting an appropriate filter constraint equation to match the metric used to measure the performance of the filter.
we give an unified theoretical basis for developing these filters. This family of filters include some of the existing linear and nonlinear filters.
Experimental demonstration of a chirp-modulated joint transfrom correlator using separate input SLMs
Application of wire-grid silicon liquid crystal light valve in nonlinear optical pattern recognition
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You will have access to both the presentation and article (if available).
This course provides an introduction to the principles that govern the acquisition of 3D images with optical microscopes. Specifically, it provides attendees with practical knowledge to understand the limitations of conventional microscopes when imaging 3D samples, as well as the principles of different emerging microscopy techniques with optical-sectioning capacity
The course will include three parts. In the first part, we describe the fundamentals of 2D imaging processes in conventional microscopes, and why they are not well adapted for imaging 3D samples. In the second part, we will focus on different optical-sectioning microscopy techniques, such as confocal, 4Pi, multi-photon, and structured illumination microscopy. In the third part, we will focus on emerging approaches, including one-shot 3D microscopes, digital holographic microscopes, etc. The attendee will benefit from a concise and realistic overview of microscopy procedures, which may help them to select the adequate microscope for various applications. The course will provide discussions of optical hardware and various practical applications of 3D optical microscopy. Also, discussions and examples will be presented on the benefits of 3D optical microscopy over conventional 2D optical microscopy.
The course will review the following fundamentals which are necessary to understand, design, and analyze 3D imaging and display systems. The course helps the students to understand how 3D imaging works, what are the fundamentals, how to use optics to implement 3D displays, how to improve performance in 3D imaging systems and/or product lines and new product development programs, how to increase optical performance, or simply find new solutions to existing technological problems.
Fundaments of Geometrical Optics: propagation of rays in transparent materials, refraction of rays in plane and spherical diopters, image formation with lenses, combination of lenses, aperture and field limitation, law of lenses: image position and magnification, examples and applications.
Wave theory of image formation: the plane wave and the spherical waves, the wavefield as linear superposition of spherical waves, propagation of wavefields though converging lenses, waves through telecentric optical systems, image formation analyzed in terms of wave optics: the concepts of PSF, spatial resolution, OTF and frequency cut-off, light diffracted through periodic screens, examples and applications.
Wave and ray theory of 3D optical capture and display systems such as plenoptic systems: capture of lightfield with an array of digital cameras: the synthetic aperture method, capture of lightfield with a plenoptic camera working in the 1.0 mode, capture of lightfield with a plenoptic camera working in the 2.0 mode, algorithms for the calculation of views and for the reconstruction in depth, examples and applications, implementation of synthetic-aperture setup, and implementation of a plenoptic camera.
Image processing and recognition is one of the important applications of information systems. This course is aimed at people interested to learn practical applications of image processing techniques applied to real-time applications including biomedical image processing and image recognition problems. This course will review fundamentals of digital image processing, imaging systems, image recognition, statistical filtering for image processing, fundamentals of wavelet transforms for image processing, fundamentals of neural networks for image processing, and recent advances in image recognition techniques. The course will present examples on applications of these techniques in real-time pattern recognition, target tracking, classification, and real-time biometrics recognition.
This course provides an introduction to the signal processing methods used to increase image resolution. Specifically, it provides attendees with the practical knowledge to estimate the benefits of using super resolution in an imaging system as well as the guidance to select the right super resolution method for a given application.
The course is divided into three parts. In the first part, we describe the fundamental limits to resolution in an imaging system and establish the necessity of using signal processing as a mean to achieve super resolution. In the second part we focus on different super resolution techniques. Specifically, we cover defocus based techniques, zoom based techniques, photometry based techniques and edge enhancement based techniques.
In the third part of the course we provide some real life examples from various imaging fields to establish how the super resolution techniques work. The attendee will therefore benefit from a concise and realistic overview of current signal processing methods for super resolution, and thus be able to make the right decision when it comes to accessing the potential use of super resolution for a specific product development.
This course is aimed at people interested in learning practical applications of image processing techniques applied to real-time applications including biomedical image processing and image recognition problems. This course reviews fundamentals of digital image processing, imaging systems, image recognition, statistical filtering for image processing, fundamentals of wavelet transforms for image processing, fundamentals of neural networks for image processing, and recent advances in image recognition techniques. The course presents examples of applications using these techniques in real-time pattern recognition, target tracking, classification, and real-time biometrics recognition.
This course reviews the fundamentals of spatial light modulators, recent advances in light modulators, and their applications in holography. Optical information and image processing, biometrics and pattern recognition, neural networks, optical memory, security systems, products authenticity, and anti-counterfeiting are other applications addressed. A video demonstration of real-time optical systems using spatial light modulators is presented.
3-dimensional imaging is believed to be the next generation imaging technology, which will eventually replace the current plane image. In spite of the fact that the first stereoscopic viewing device was introduced on 1838, the lack of supporting technologies such as high resolution display devices, high speed electronics, high resolution viewing zone forming optics and high speed image processing deterred further development in 3-D imaging for a long time. However, the recent progress in high resolution and high density flat panel display devices as well as PC technology have enabled the construction of a number of multiview 3-D imaging systems.
This one-day course is intended for those who have an interest in 3-D imaging. Recent progress in 3-D imaging technologies and their perspectives will be presented, the theoretical background behind various 3-D imaging systems will be covered in-depth and fundamental limits imposed on the 3-D imaging process will be explained.
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