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Smartphone Camera Self-Calibration Based on Sensors Reading Consistency

Smartphone Camera Self-Calibration Based on Sensors Reading Consistency In large-scale series production the time for evaluating the camera spectral sensitivity is strongly limited and measured in units of seconds because of production and economic constraints. To estimate variation of spectral sensitivity properties, manufacturers usually precisely measure only a few sensors (the golden set) and use these measurements to perform quick estimation of any other sensor in the released pack. The main drawback of this approach is that the worst color reproduction error cannot be controlled for a particular device: instability of device production process usually causes significantly different sensors, which may not be included in the golden set. In that case the camera will work with low accuracy during the lifetime. To overcome this problem, we consider a new approach to camera spectral sensitivity estimation during its operation. The main idea is based on consistency estimation of images and average scenes spectra. Users receive such a combination of data in practice, for instance modern phone devices have built-in integral spectrometers. Also, the proposed approach can be considered in the scope of classical problem statement of spectral sensitivity estimation with color charts. In the paper we investigated the accuracy of the method of spectral sensitivity estimation based on the basis calculation with singular value decomposition of the sensitivities from the golden set in combination with different types of regularization. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Optical Memory and Neural Networks Springer Journals

Smartphone Camera Self-Calibration Based on Sensors Reading Consistency

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References (16)

Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2022. ISSN 1060-992X, Optical Memory and Neural Networks, 2022, Vol. 31, Suppl. 1, pp. S48–S54. © Allerton Press, Inc., 2022.
ISSN
1060-992X
eISSN
1934-7898
DOI
10.3103/s1060992x22050083
Publisher site
See Article on Publisher Site

Abstract

In large-scale series production the time for evaluating the camera spectral sensitivity is strongly limited and measured in units of seconds because of production and economic constraints. To estimate variation of spectral sensitivity properties, manufacturers usually precisely measure only a few sensors (the golden set) and use these measurements to perform quick estimation of any other sensor in the released pack. The main drawback of this approach is that the worst color reproduction error cannot be controlled for a particular device: instability of device production process usually causes significantly different sensors, which may not be included in the golden set. In that case the camera will work with low accuracy during the lifetime. To overcome this problem, we consider a new approach to camera spectral sensitivity estimation during its operation. The main idea is based on consistency estimation of images and average scenes spectra. Users receive such a combination of data in practice, for instance modern phone devices have built-in integral spectrometers. Also, the proposed approach can be considered in the scope of classical problem statement of spectral sensitivity estimation with color charts. In the paper we investigated the accuracy of the method of spectral sensitivity estimation based on the basis calculation with singular value decomposition of the sensitivities from the golden set in combination with different types of regularization.

Journal

Optical Memory and Neural NetworksSpringer Journals

Published: Dec 1, 2022

Keywords: camera calibration; spectral sensitivity estimation; golden set; quality of color reproduction; color patches

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