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4 June 2020 Tunable color correction for noisy images
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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-near-infrared sensor as an example.

© 2020 SPIE and IS&T 1017-9909/2020/$28.00© 2020 SPIE and IS&T
Ryo Yamakabe, Yusuke Monno, Masayuki Tanaka, and Masatoshi Okutomi "Tunable color correction for noisy images," Journal of Electronic Imaging 29(3), 033012 (4 June 2020).
Received: 27 December 2019; Accepted: 21 May 2020; Published: 4 June 2020

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