Robust Distribution-aware Color Correction for Single-shot Images

Abstract

Color correction for photographed images is an ill-posed problem. It is also a crucial initial step towards material acquisition for inverse rendering methods or pipelines. Several state-of-the-art methods rely on reducing color differences for imaged reference color chart blocks of known color values to devise or optimize their solution. In this paper, we first establish through simulations the limitation of this minimality criteria which in principle results in overfitting. Next, we study and propose a few spatial distribution measures to augment the evaluation criteria. Thereafter, we propose a novel patch-based, white-point centric approach that processes luminance and chrominance information separately to improve on the color matching task. We compare our method qualitatively with several state-of-the art methods using our augmented evaluation criteria along with quantitative examinations. Finally, we perform rigorous experiments and demonstrate results to clearly establish the benefits of our proposed method.