As-projective-as-possible bias correction for illumination estimation algorithms

Afifi, Mahmoud, Punnappurath, Abhijith, Finlayson, Graham and Brown, Michael S. (2019) As-projective-as-possible bias correction for illumination estimation algorithms. Journal of the Optical Society of America A: Optics and Image Science, and Vision, 36 (1). pp. 71-78. ISSN 1084-7529

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Abstract

Illumination estimation is the key routine in a camera’s onboard auto-white-balance (AWB) function. Illumination estimation algorithms estimate the color of the scene’s illumination from an image in the form of an R, G, B vector in the sensor’s raw-RGB color space. While learning-based methods have demonstrated impressive performance for illumination estimation, cameras still rely on simple statistical-based algorithms that are less accurate but capable of executing quickly on the camera’s hardware. An effective strategy to improve the accuracy of these fast statistical-based algorithms is to apply a post-estimate bias-correction function to transform the estimated R, G, B vector such that it lies closer to the correct solution. Recent work by Finlayson [Interface Focus 8, 20180008 (2018)] showed that a bias-correction function can be formulated as a projective transform because the magnitude of the R, G, B illumination vector does not matter to the AWB procedure. This paper builds on this finding and shows that further improvements can be obtained by using an as-projective-as-possible (APAP) projective transform that locally adapts the projective transform to the input R, G, B vector. We demonstrate the effectiveness of the proposed APAP bias correction on several well-known statistical illumination estimation methods. We also describe a fast lookup method that allows the APAP transform to be performed with only a few lookup operations.

Item Type: Article
Uncontrolled Keywords: electronic, optical and magnetic materials,atomic and molecular physics, and optics,computer vision and pattern recognition ,/dk/atira/pure/subjectarea/asjc/2500/2504
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: LivePure Connector
Date Deposited: 08 Jul 2019 15:30
Last Modified: 22 Apr 2020 07:41
URI: https://ueaeprints.uea.ac.uk/id/eprint/71664
DOI: 10.1364/JOSAA.36.000071

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