A scratch removal method

Michal Haindl; Stanislava Šimberová

Kybernetika (1998)

  • Volume: 34, Issue: 4, page [423]-428
  • ISSN: 0023-5954

Abstract

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We present a new type of scratch removal algorithm based on a causal adaptive multidimensional prediction. The predictor use available information from the failed pixel surrounding due to spectral and spatial correlation of multispectral data but not any information from failed pixel itself. Predictor parameters cannot be directly identified so a special approximation is introduced.

How to cite

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Haindl, Michal, and Šimberová, Stanislava. "A scratch removal method." Kybernetika 34.4 (1998): [423]-428. <http://eudml.org/doc/33372>.

@article{Haindl1998,
abstract = {We present a new type of scratch removal algorithm based on a causal adaptive multidimensional prediction. The predictor use available information from the failed pixel surrounding due to spectral and spatial correlation of multispectral data but not any information from failed pixel itself. Predictor parameters cannot be directly identified so a special approximation is introduced.},
author = {Haindl, Michal, Šimberová, Stanislava},
journal = {Kybernetika},
keywords = {multidimensional prediction; regression; scratch reconstruction; multidimensional prediction; regression; scratch reconstruction},
language = {eng},
number = {4},
pages = {[423]-428},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A scratch removal method},
url = {http://eudml.org/doc/33372},
volume = {34},
year = {1998},
}

TY - JOUR
AU - Haindl, Michal
AU - Šimberová, Stanislava
TI - A scratch removal method
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 4
SP - [423]
EP - 428
AB - We present a new type of scratch removal algorithm based on a causal adaptive multidimensional prediction. The predictor use available information from the failed pixel surrounding due to spectral and spatial correlation of multispectral data but not any information from failed pixel itself. Predictor parameters cannot be directly identified so a special approximation is introduced.
LA - eng
KW - multidimensional prediction; regression; scratch reconstruction; multidimensional prediction; regression; scratch reconstruction
UR - http://eudml.org/doc/33372
ER -

References

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  1. al R. Bernstein et, Analysis and processing of Landsat-4 sensor data using advanced image processing techniques and technologies, IEEE Trans. Geosci. 22 (1984), GE-22, 192–221 (1984) 
  2. Broemeling L. D., Bayesian Analysis of Linear Models, Dekker, New York 1985 Zbl0564.62020MR0772380
  3. Haindl M., Šimberová S., A multispectral image line reconstruction method, In: Theory & Applications of Image Analysis (P. Johansen and S. Olsen, eds.), World Scientific, Singapore 1992 
  4. Haindl M., Šimberová S., A high–resolution radiospectrograph image reconstruction method, Astronom. and Astrophys., Suppl. Ser. 115 (1996), 189–193 (1996) 
  5. Venetianer P. L., Werblin F., Roska T., Chua L. O., 10.1109/81.386161, IEEE Trans. Circuit Systems CS-42 (1995), 278–284 (1995) DOI10.1109/81.386161

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