About Me
Received my B.Sc. and Ph.D. degrees in Electrical Engineering from Tel Aviv University, Israel where I am Full Professor of electro-optics.
Has authored more than 200 technical articles, 3 book chapters, and is the holder of more than 40 patents all of them have been commercialized.
Founder of successful opto-electronics startup companies (e.g. Civcom and Eyesquad) and served as their CEO. Civcom Inc. was acquired by Padtec S/A of Brazil, Eyesquad was acquired by Tessera Inc (NASDAQ symbol: TSRA).
Founded Corephotonics where he serves as the CEO. Corephotonics was acquired by Samsung. Recently he founded Unispectral and Multiview which are spin-offs of Tel Aviv University.
Since January 2008 till December 2010, I was the Chief Scientist of the Israeli Ministry of Science. Also acted 6 years as the Co-Chair of GIF. At present he serves as Vice Dean for Research of the Faculty of Engineering.
Gathers the new Tel Aviv University Center for Entrepreneurship and serves as the Head of Zimin Institute for Engineering Solutions Advancing Better Lives.
Areas Of Research
• Computational photography and its use for miniature imaging systems
• Advanced 3-D sensing and mapping devices based on a single aperture
• AI based image processing algorithms and pipelines
• Advanced authentication systems and methods
• Continues optical sensing of bio-mechanical parameters
• Image processing algorithms for deep-fake
My Latest Publications
Deep Sparse Light Field Refocusing
Shachar Ben Dayan, David Mendlovic, Raja Giryes
Computer Science > Computer Vision and Pattern Recognition
Light field photography enables to record 4D images, containing angular information alongside spatial information of the scene. One of the important applications of light field imaging is post-capture refocusing. Current methods require for this purpose a dense field of angle views; those can be acquired with a micro-lens system or with a compressive system.
Both techniques have major drawbacks to consider, including bulky structures and angular-spatial resolution trade-off. We present a novel implementation of digital refocusing based on sparse angular information using neural networks. This allows recording high spatial resolution in favor of the angular resolution, thus, enabling to design compact and simple devices with improved hardware as well as better performance of compressive systems.
We use a novel convolutional neural network whose relatively small structure enables fast reconstruction with low memory consumption. Moreover, it allows handling without re-training various refocusing ranges and noise levels. Results show major improvement compared to existing methods.
