Tunable color correction between linear and polynomial models for noisy images
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi · 2017
Linear color correction (LCC) and polynomial color correction (PCC) are widely used in a camera imaging pipeline. PCC generally achieves lower colorimetric errors than LCC. However, if an image contains noise, PCC amplifies the noise more severely than LCC. Consequently, there is a trade-off between LCC and PCC in the presence of noise. In this paper, we propose a novel framework for color correction, which we call tunable color correction (TCC). TCC enables us to tune a color correction matrix between linear and polynomial models by a tuning parameter. We also present a way of selecting a suitable parameter value based on the mean squared error calculation model for PCC. Experimental results demonstrate that TCC effectively balances the trade-off and outperforms both LCC and PCC for noisy images.