Title:
Novel Superresolution Methods for Computational Photography and Soil Moisture Remote Sensing

dc.contributor.advisor Romberg, Justin
dc.contributor.author Beale, Kevin Daniel
dc.contributor.committeeMember Bras, Rafael L.
dc.contributor.committeeMember Lanterman, Aaron D.
dc.contributor.committeeMember Wang, Jingfeng
dc.contributor.committeeMember Anderson, David V.
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2023-07-26T18:50:32Z
dc.date.available 2023-07-26T18:50:32Z
dc.date.created 2023-05
dc.date.issued 2023-05-22
dc.date.submitted May 2023
dc.date.updated 2023-07-26T18:50:32Z
dc.description.abstract The objective of this work is to solve two real-world superresolution problems of fundamental importance: extending the resolutions of non-diffraction-limited imaging systems, and estimating soil moisture globally at high spatiotemporal resolution. We approach these problems as instances of multi-measurement superresolution and single-image superresolution, and develop specialized methods tailored to the specifics of each problem. To solve the first problem, we augment a conventional imaging system with a programmable mask and defocused lens, allowing us to capture superresolved images beyond the resolutions of both mask and sensor by factors greater than 4x without the use of mechanical motion or an image model. To solve the second problem, we develop a robust method for enhancing the spatial resolution of soil moisture retrievals from NASA's Soil Moisture Active Passive (SMAP) satellite by using low rank modeling to both recover missing values and implement a resolution enhancement method based on learning relationships between dominant high-resolution patterns and low-resolution covariates at all locations globally.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri https://hdl.handle.net/1853/72509
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Superresolution
dc.subject Computational imaging
dc.subject Remote sensing
dc.subject Soil moisture
dc.title Novel Superresolution Methods for Computational Photography and Soil Moisture Remote Sensing
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Romberg, Justin
local.contributor.corporatename College of Engineering
local.contributor.corporatename School of Electrical and Computer Engineering
relation.isAdvisorOfPublication 23ff0d70-23a6-4f87-bde3-5f3427d03dfe
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
thesis.degree.level Doctoral
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
BEALE-DISSERTATION-2023.pdf
Size:
12.04 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
3.86 KB
Format:
Plain Text
Description: