Description

Fixed Pattern Noise Non-Uniformity Correction through K-Means Clustering

Fixed pattern noise removal from imagery by software correction is a practical approach compared to a physical hardware correction because it allows for correction post-capture of the imagery. Fixed pattern noise presents a unique challenge for de-noising techniques as the noise does not present itself where large number statistics are effective. Traditional noise removal techniques such as blurring or despeckling produce poor correction results because of a lack of noise identification. Other correction methods developed for fixed pattern noise can often present another problem of misidentification of noise. This problem can result in introducing secondary artifacts that can disrupt the imagery and leave the resulting image worse than the uncorrected image. This underlying issue of poor noise identification stems from strong assumptions globally and locally in the imagery. A proposed approach utilizing image intensity clustering will blend local and global information to find a nuanced correction value on a row-by-row basis. The proposed algorithm’s evaluation will be against multiple other correction methods developed for fixed pattern noise removal through a synthetic suite of imagery. The suite is founded on clean images and expanded by varied synthetic noise types introduced by algorithmic means. Images will be evaluated pixel by pixel, row mean by row mean, and with and without a scene intensity bias correction for validation of noise correction.

Author ORCID Identifier

John M. Edwards https://orcid.org/ 0000-0002-0882-312X

OCLC

1250025722

Document Type

Dataset

DCMI Type

Dataset

File Format

.zip, .txt

Viewing Instructions

Files will need to be decompressed

Publication Date

5-6-2021

Publisher

Utah State University

Methodology

The base images where sourced mainly from a NASA geo landsat tool and the corrupted images where generated from that base collection through manipulation Noise was introduced through algorithmic means.

Language

eng

Code Lists

AN Agminated Noise

GNNP Global Non-Periodic Noise

GNP Global Periodic Noise

LNNP Local Non-Periodic Noise

LNP Local Periodic Noise

PBN Periodic Banding Noise

SN Single Noise

Comments

Noise if representative of poorly calibrated scanner style imaging sensors.

Disciplines

Astrophysics and Astronomy | Computer Sciences | Instrumentation | Other Computer Sciences

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Identifier

https://doi.org/10.26078/w01a-5k06

Checksum

a2ae9474c8756a21423a405584d0ada7

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Additional Files

README.txt (2 kB)
MP5: b0524b2a0d1b098b7dbfbadecd699d41

all_image .zip (520576 kB)
MD5: b902030bcc99b2043336fcb88457b344

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