Compressive super-localization

Localization accuracy is a fundamental quantity of an imaging system, since it often determines the performance (e.g. effective resolution, sensitivity) of the system. This thesis studies the influence on localization accuracy of finite sampling rate, number of samples, and noise in the data acquisition process. Two classes of super-localization problems will be investigated. The first class of problem aims to improve the accuracy in localizing and tracking the physical position of an object of interest. For lateral localization problems, the key parameters to consider are the pixel size as compared to the size of the point spread function (PSF) of the imaging system, and to the amount of motion of the object of interest. If the pixel size is larger than PSF or object motion, accurate localization essentially becomes solving an under-sampling problem. In this thesis, a compressive holography algorithm is proposed to localize smaller than pixel size motion. 1/45 sub-pixel motion has been successfully detected. For axial localization problems, the localization accuracy will be affected not only by the pixel size and number of pixels, but also by the imaging geometry. Traditional axial localization methods are fundamentally limited by the finite numerical aperture of the optical system. In this thesis, a class of compressive reconstruction method that exploits the "sparse" prior knowledge about the object in order to alleviate the missing angular information has been investigated and 1/16 depth of field (DOF) axial displacement was successfully extracted. Successful implementation of the compressive holography based super-localization technique has been applied to image biomimetic sensors inspired by harbor sea seal whiskers for studying vortex-induced vibrations and wake-induced vibrations. The second class of super-localization problem under investigation is to detect weak signals buried under strong background and noise. A compressive reconstruction method that is able to detect signals captured with extremely low signal-to-noise ratio (SNR) and signal-to-background ratio (SBR), by exploiting different "sparsities" in the respective signal and background subspaces has been demonstrated.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Liu, Yi, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• George Barbastathis.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/103483

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/103483