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ToolsFinlayson, Graham, Mackiewicz, Michal and Hurlbert, Anya (2015) Colour correction using root-polynomial regression. IEEE Transactions on Image Processing, 24 (5). pp. 1460-1470. ISSN 1057-7149
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Abstract
Cameras record three color responses (RGB) which are device dependent. Camera coordinates are mapped to a standard color space, such as XYZ-useful for color measurement-by a mapping function, e.g., the simple 3×3 linear transform (usually derived through regression). This mapping, which we will refer to as linear color correction (LCC), has been demonstrated to work well in the number of studies. However, it can map RGBs to XYZs with high error. The advantage of the LCC is that it is independent of camera exposure. An alternative and potentially more powerful method for color correction is polynomial color correction (PCC). Here, the R, G, and B values at a pixel are extended by the polynomial terms. For a given calibration training set PCC can significantly reduce the colorimetric error. However, the PCC fit depends on exposure, i.e., as exposure changes the vector of polynomial components is altered in a nonlinear way which results in hue and saturation shifts. This paper proposes a new polynomial-type regression loosely related to the idea of fractional polynomials which we call root-PCC (RPCC). Our idea is to take each term in a polynomial expansion and take its kth root of each k-degree term. It is easy to show terms defined in this way scale with exposure. RPCC is a simple (low complexity) extension of LCC. The experiments presented in this paper demonstrate that RPCC enhances color correction performance on real and synthetic data.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Depositing User: | Pure Connector |
| Date Deposited: | 17 Mar 2015 06:38 |
| Last Modified: | 20 Oct 2022 22:35 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/52673 |
| DOI: | 10.1109/TIP.2015.2405336 |
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