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Radiometric calibration of digital cameras using sparse Gaussian processes

  • Digital cameras are used in a large variety of scientific and industrial applications. For most applications the acquired data should represent the real light intensity per pixel as accurately as possible. However, digital cameras are subject to different sources of noise which distort the resulting image. Noise includes photon noise, fixed pattern noise and read noise. The aim of the radiometric calibration is to improve the quality of the resulting images by reducing the influence of the different types of noise on the measured data. In this paper, a new approach for the radiometric calibration of digital cameras using sparse Gaussian process regression is presented. Gaussian process regression is a kernel based supervised machine learning technique. It is used to learn the response of a camera system from a set of training images to allow for the calibration of new images. Compared to the standard Gaussian process method or flat field correction our sparse approach allows for faster calibration and higher reconstruction quality.

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Metadaten
Author:Michael Grunwald, Jens Gansloser, Matthias O. FranzORCiDGND
URL:https://www.researchgate.net/publication/310313872_Radiometric_calibration_of_digital_cameras_using_sparse_Gaussian_processes
ISBN:978-3-00-053918-3
Parent Title (German):22. Workshop Farbbildverarbeitung : 29.-30. September 2016, Ilmenau
Publisher:Ilmenau
Place of publication:Zentrum für Bild- und Signalverarbeitung
Document Type:Conference Proceeding
Language:English
Year of Publication:2016
Release Date:2018/11/21
First Page:23
Last Page:35
Open Access?:Ja
Licence (German):License LogoUrheberrechtlich geschützt