EE Seminar: Unified Functional Framework for Restoration of Image Sequences Degraded by Atmospheric Turbulence

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Speaker: Naftali Zon
M.Sc. student under the supervision of Prof. Nahum Kiryati

Wednesday, June 22nd, 2016 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering

Unified Functional Framework for Restoration of Image Sequences Degraded by Atmospheric Turbulence

Atmospheric turbulence between the scene and the camera degrades image quality. Diverse algorithms estimate the latent image from an image sequence of a static scene. The flow of these algorithms reflects various concepts, but their theoretical foundations are not sufficiently clear. Recent algorithms have yielded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this thesis is to analyze recent turbulence restoration algorithms and to suggest a unified variational framework to address the turbulence restoration problem. A unified functional quantifies the association between a given image sequence and a candidate latent image restoration. Minimizing the functional using advanced numerical methods leads to a general algorithmic flow. I show that various known algorithms can be derived as special cases of the general approach. Furthermore, I show that building-blocks used in turbulence recovery algorithms, such as optical flow estimation and blind deblurring, are called for by the general model. The main contribution of this thesis is the establishment of a unified theoretical framework for the restoration of turbulence-degraded images. It leads to novel turbulence recovery algorithms as well as to better understanding of known ones. Along the way we develop a state of the art method for general-purpose blind deconvolution and a novel method for optical flow estimation.