סמינר מחלקתי אלקטרוניקה פיזיקאלית : Ofir Nabati

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