Date of Award:
8-2021
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
John Edwards
Committee
John Edwards
Committee
Nicholas S. Flann
Committee
Mario Harper
Abstract
Imagery obtained with poorly calibrated sensors is often corrupted with fixed pattern noise. Fixed pattern noise presents itself through a non-uniform distribution and therefore is hard to target in noise removal. Traditional noise removal techniques assume that the noise is uniformly distributed and subsequently produces inadequate corrections. Noise correction methods that target fixed pattern noise rely on dynamically identifying present noise and adjust correction values appropriately using nearby information or general assumptions about the image’s composition. If noise identification is not accurate, the correction values will also suffer from low accuracy. Inaccurate correction values can affect the imagery’s quality, and in some cases, produce a corrected image worse off than an uncorrected image. The proposed algorithm utilizes local and global information to find more accurate correction values on a row-by-row basis. This paper will also introduce a standard dataset and evaluation metrics for comparison against other established non-uniformity correction methods.
Checksum
7673a87b5f8ad3db30381ea0c9b4af9b
Recommended Citation
Imperial, Andres, "Fixed Pattern Noise Non-Uniformity Correction Through K-Means Clustering" (2021). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 8112.
https://digitalcommons.usu.edu/etd/8112
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