Color Constancy by Reweighting Image Feature Maps.

In this study, a novel illuminant color estimation framework is proposed for computational color constancy, which incorporates the high representational capacity of deep-learningbased models and the great interpretability of assumptionbased models. The well-designed building block, feature map reweight unit (ReWU), helps to achieve comparative accuracy on benchmark datasets with respect to prior state-of-the-art deep learning based models while requiring more compact model size and cheaper computational cost. In addition to local color estimation, a confidence estimation branch is also included such that the model is able to simultaneously produce point estimate and its uncertainty estimate, which provides useful clues for local estimates aggregation and multiple illumination estimation.

Image quality degradation of object-color metamer mismatching in digital camera color reproduction.

Metamer mismatching is a phenomenon where two objects that are colorimetrically indistinguishable under one lighting condition become distinguishable under another one. Due to the unavailability of spectral information, metamer mismatching introduces an inherent uncertainty into cameras' color reproduction. To investigate the degree of image quality degradation by the metamer mismatching, a large spectral reflectance database was compiled in this study to search the object-color metamers sets of the spectra in hyperspectral images.

Camera response prediction for various capture settings using the spectral sensitivity and crosstalk model.

In this paper, a camera response formation model is proposed to accurately predict the responses of images captured under various exposure settings. Differing from earlier works that estimated the camera relative spectral sensitivity, our model constructs the physical spectral sensitivity curves and device-dependent parameters that convert the absolute spectral radiances of target surfaces to the camera readout responses. With this model, the camera responses to miscellaneous combinations of surfaces and illuminants could be accurately predicted.