Learning efficient image processing pipelines
Name
1084487535-MIT.pdf
Description
Full printable version
Size
14.36 MB
Format
Adobe PDF
Checksum (MD5)
cf30180aed97f9f173e257ed0d20abef
Author(s)
Gharbi, Michael (Michael Yanis)
Advisor(s)
Frédo Durand.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
The high resolution of modern cameras puts significant performance pressure on image processing pipelines. Tuning the parameters of these pipelines for speed is subject to stringent image quality constraints and requires significant efforts from skilled programmers. Because quality is driven by perceptual factors with which most quantitative image metrics correlate poorly, developing new pipelines involves long iteration cycles, alternating between software implementation and visual evaluation by human experts. These concerns are compounded on modern computing platforms, which are increasingly mobile and heterogeneous. In this dissertation, we apply machine learning towards the design of high-performance, high-fidelity algorithms whose parameters can be optimized automatically on large-scale datasets via gradient based methods. We present applications to low-level image restoration and high performance image filtering on mobile devices.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages [125]-138).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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