Tunable Color Correction for Noisy Images
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi
SPIE Journal of Electronic Imaging, vol. 29, no. 3, pp. 033012-1-24, 2020
Proposed Algorithm Overview
Abstract
Color correction is one of the most essential camera imaging operations that transforms a camera-specific RGB color space to a standard color space, typically the XYZ or the sRGB color space. Linear color correction (LCC) and polynomial color correction (PCC) are two widely used methods; they perform the color space transformation using a color correction matrix. Owing to the use of high-order terms, PCC generally achieves lower colorimetric errors than LCC. However, PCC amplifies noise more severely than LCC. Consequently, for noisy images, there exists a trade-off between LCC and PCC regarding color fidelity and noise amplification. We propose a color correction framework called tunable color correction (TCC) that
enables us to tune the color correction matrix between the LCC and the PCC models. We also derive a mean squared error calculation model of PCC that enables us to select the best trade-off balance in the TCC framework. We experimentally demonstrate that TCC effectively balances the trade-off for noisy images and outperforms LCC and PCC.We also generalize TCC to multispectral cases and demonstrate its effectiveness by taking the color correction for an RGB-nearinfrared sensor as an example.
Publications
- Tunable Color Correction for Noisy Images [PDF]
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi
SPIE Journal of Electronic Imaging, vol. 29, no. 3, pp. 033012-1-24, June, 2020. - Tunable Color Correction Between Linear and Polynomial Models for Noisy Images [PDF], [Poster]
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi
IEEE International Conference on Image Processing (ICIP), pp. 3125-3129, September, 2017.