We test this approach on the proposed method and show that it can indeed be used to avoid several extreme error cases and, thus, improves the practicality of the proposed technique.
Computational color constancy is a preprocessing step used in many camera systems.
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC).
In this paper, we describe a new large dataset for illumination estimation.
To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention.
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem.
We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering.
We introduce a novel formulation of temporal color constancy which considers multiple frames preceding the frame for which illumination is estimated.
Computational color constancy that requires esti- mation of illuminant colors of images is a fundamental yet active problem in computer vision, which can be formulated into a regression problem.