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Proceedings Paper

Quantitative image restoration
Author(s): Irina Gladkova; Michael Grossberg; Fazlul Shahriar
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Paper Abstract

Even with the most extensive precautions and careful planning, space based imagers will inevitably experience problems resulting in partial data corruption and possible loss. Such a loss occurs, for example, when individual image detectors are damaged. For a scanning imager this results in missing lines in the image. Images with missing lines can wreak havoc since algorithms not typically designed to handle missing pixels. Currently the metadata stores the locations of missing data, and naive spatial interpolation is used to fill it in. Naive interpolation methods can create image artifacts and even statistically or physically implausible image values. We present a general method, which uses non-linear statistical regression to estimate the values of the missing data in a principled manner. A statistically based estimate is desirable because it will preserve the statistical structure of the uncorrupted data and avoid the artifacts of naive interpolation. It also means that the restored images are suitable as input for higher-level statistical products. Previous methods replaced the missing values with those of a single closely related band, by applying a function or lookup table. We propose to use the redundant information in multiple bands to restore the lost information. The estimator we present in this paper uses values in a neighborhood of the pixel to be estimated, and propose a value based on training data from the uncorrupted pixels. Since we use the spatial variations in other channels, we avoid the blurring inherent spatial interpolation, which have implicit smoothness priors.

Paper Details

Date Published: 13 May 2010
PDF: 12 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769519 (13 May 2010); doi: 10.1117/12.851731
Show Author Affiliations
Irina Gladkova, The City College of New York, NOAA/CREST (United States)
Michael Grossberg, The City College of New York, NOAA/CREST (United States)
Fazlul Shahriar, The City College of New York, NOAA/CREST (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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