Color Correction Using Root-Polynomial Regression


Cameras record three color responses ( $RGB$ ) which are device dependent. Camera coordinates are mapped to a standard color area, like XYZ—helpful for color measurement—by a mapping function, e.g., the straightforward $3times 3$ linear transform (typically derived through regression). This mapping, that we tend to can check with as linear color correction (LCC), has been demonstrated to work well in the number of studies. But, it will map $RGBtexts$ to XYZs with high error. The advantage of the LCC is that it's freelance of camera exposure. An alternative and doubtless additional powerful methodology 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 coaching set PCC will considerably cut back the colorimetric error. However, the PCC match depends on exposure, i.e., as exposure changes the vector of polynomial elements is altered during a nonlinear method which ends up in hue and saturation shifts. This paper proposes a brand new polynomial-sort regression loosely related to the idea of fractional polynomials which we call root-PCC (RPCC). Our plan is to take each term in a very polynomial enlargement and take its $k$ th root of each $k$ -degree term. It is easy to show terms defined during this means scale with exposure. RPCC is a easy (low complexity) extension of LCC. The experiments presented during this p- per demonstrate that RPCC enhances color correction performance on real and synthetic data.

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