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ToolsAfifi, 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
Full text not available from this repository. (Request a copy)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 |
| UEA Research Groups: | Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Colour and Imaging Lab |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 08 Jul 2019 15:30 |
| Last Modified: | 21 Apr 2023 00:01 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/71664 |
| DOI: | 10.1364/JOSAA.36.000071 |
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