Image restoration using sub-images and confidence intervals
type: CreativeWork
description: In applications, images are recorded, blurred, and noisy. This work aims to compute confidence intervals for quantifying the uncertainty of a reconstructed image. By partitioning the image into sub-images, the algorithm remains effective while becoming more efficient. Sub-images also provide flexibility to focus on a local reconstruction, focusing on the parts of the image with the most importance. Many time-sensitive applications require signal restoration and would benefit from this reconstruction process.
abstract: In applications, images are recorded, blurred, and noisy. This work aims to compute confidence intervals for quantifying the uncertainty of a reconstructed image. By partitioning the image into sub-images, the algorithm remains effective while becoming more efficient. Sub-images also provide flexibility to focus on a local reconstruction, focusing on the parts of the image with the most importance. Many time-sensitive applications require signal restoration and would benefit from this reconstruction process.
author: Babcock, Jasen
keywords: Mathematics, Other Applied Mathematics, Theory and Algorithms
date published: 2022-01-01T08:00:00Z
date modified: 2026-07-24T02:12:39Z
provider: Eastern Washington University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Eastern Washington University