You are invited to attend a lecture

Meta-Engineering of Light in the Linear, Nonlinear and Quantum Optical Regimes

:By

Nir Shitrit

University of California, Berkeley

Abstract

Through subwavelength structuring of materials, metamaterials have shown exquisite control over electromagnetic properties, experimentally demonstrating phenomena not found in nature such as a negative index of refraction. Metasurfaces constitute a more recent branch of metamaterials research, in which two-dimensional ultrathin arrays of engineered nanostructures mold optical wavefronts at will, aiming to revolutionize optical designs by realizing virtually flat, lightweight optics that replaces bulky optical elements. In this talk, we will detail how photonic meta-engineering has advanced metasurfaces as a new paradigm for linear, nonlinear and quantum optics. First, we demonstrated beam shaping via metasurfaces based on optical nanoantennas for visible light, where the photon spin is utilized as a new degree of freedom. We also used metasurfaces as architecture to reveal the role of surface symmetry properties on light-matter interactions and, more specifically, observed the optical analog of the electronic Rashba effect—that is, spin-split dispersion due to broken inversion symmetry. In the nonlinear regime, we explored functionalities which cannot be fundamentally achieved by linear metasurfaces, and demonstrated two-way asymmetric transport of light at nonlinear metasurfaces, paving the way for ultrathin optical diodes. Finally, we showed the extension of metasurfaces to the quantum optical regime, while theoretically demonstrating that a metasurface can efficiently mediate quantum entanglement between two macroscopically separated quantum emitters at the chip level. To conclude, we propose several future directions of structures and materials by design including non-Markovian metasurfaces, mechanical metamaterials, and innovating optoelectronic devices via metastructures, which address the significant impact of meta-engineering on science and technology.

Bio: Nir Shitrit is a postdoctoral fellow at the University of California, Berkeley. He received his Ph.D. in 2014, in Mechanical Engineering, from the Technion -Israel Institute of Technology. His research interests lie at the interface of engineering and applied physics and focus on optics and photonics and, more specifically, on nano-engineered structures and materials with a custom-designed electromagnetic response.  

On Thursday, January 10, 2019, 15:00

Room 011, Kitot Building

Speaker: Nir Zarrabi-Itzhak

M.Sc. student under the supervision of Prof. Shai Avidan and Prof. Yael Moses

Wednesday, January 2^nd, 2019 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

CrowdCam: Dynamic Region Segmentation
 

Abstract

            We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane. Quite often, these regions are the most interesting parts of an image. CrowdCam images is a set of images of the same dynamic event, captured by a group of non-collaborating users. Almost every event of interest today is captured this way. This new type of images raises the need to develop new algorithms tailored specifically for it. We propose an algorithm that segments the dynamic regions in CrowdCam images. The proposed algorithm combines cues that are based on geometry, appearance and proximity. First, geometric reasoning is used to produce rough score maps that determine, for every pixel, how likely it is to be the projection of a static or dynamic scene point. These maps are noisy because CrowdCam images are usually few and far apart both in space and in time. Then, we use similarity in appearance space and proximity in the image plane to encourage neighboring pixels to be labeled similarly as either static or dynamic. We define an objective function that combines all the cues and solves it using an MRF solver. The proposed method was tested on publicly available CrowdCam datasets, as well as a new and challenging dataset we collected. Our results are better than the current state-of-the-art.

You are invited to attend a department seminar on

Advanced Techniques for Color Light Field

Reconstruction and Depth Estimation from Compressed Measurements

:By

Ofir Nabati

MSc student under the supervision of Prof. David Mendlovic and Dr.Raja Giryes

Abstract

In the last decade, the usage of digital cameras has grown exponentially for various reasons such as photography, communication and security. While conventional cameras allow for capturing the spatial information of the scene, light field

photography allows to capture the angular information as well. By doing so, it

enables for applications such as refocusing and depth estimation.

The notion of light field photography was present almost a century ago. Since

then, there has been no major breakthrough in the transition from theory into wide

usage, mainly because suggested solutions suffered heavily in terms of loss of resolution, computational time and light efficiency. One of its drawbacks is the need

for multi-lens in the imaging. To compensate that, compressed light field photography has been proposed to tackle the trade-offs between the spatial and angular

resolutions. It obtains by only one lens, a compressed version of the regular multilens system. The acquisition system consists of a dedicated hardware followed by

a decompression algorithm, which relies on the theory of compressed sensing and

sparse coding techniques. The reconstruction process usually suffers from high

computational time. In this thesis, we review various methods for reconstruction of compressed light fields and also propose a computationally efficient deep

learning based algorithm that recovers a high-quality color light field from a single coded image. Unlike previous works, we compress the color channels as well,

removing the need for a CFA in the imaging system.

Our approach outperforms existing solutions in terms of recovery quality and

computational complexity. We propose also a neural network for depth map extraction based on the decompressed light field, which is trained in an unsupervised

manner without the ground truth depth map. We also show the implementation

and performance of our algorithm in a real compressed light field camera prototype, which is significantly smaller and cheaper compared to existing commercial

light field cameras.

On Thursday, January 10, 2018, 14:00

Room 011, EE-Class Building