1. Dataset Title: Standard Non-Uniform Noise Dataset 2. Name and contact information of PI: Andres Imperial Utah State University 412 E 2170 N North Logan, UT 84341 andres.imperial@live.com 3. Name and contact information of Co-PI: John Edwards Utah State University john.edwards@usu.edu 7. Project summary, description or abstract: 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. 8. Brief description of collection and processing of data: 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. 9. Description of files: Dataset comprises of 50 base images (jpeg) and 350 noise corrupted (jpeg 2000). 10. Definition of acronyms, codes, and abbreviations: 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 11. Description or definition any other unique information that would help others use your data: Noise if representative of poorly calibrated scanner style imaging sensors. 13. Special software required to use data: Software able to decompress jpeg 2000 images This DATA was derived from other sources.