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As a novel method eliminating chromatic aberration on objects, computationalcolor constancy has becoming a fundamental prerequisite for many computervision applications. Among algorithms performing this task, the learning-basedones have achieved great success in recent years. However, they fail to fullyconsider the spatial information of images, leaving plenty of room forimprovement of the accuracy of illuminant estimation. In this paper, byexploiting the spatial information of images, we propose a color constancyalgorithm called Attention Dense Color Constancy (ADCC) using convolutionalneural network (CNN). Specifically, based on the 2D log-chrominance histogramsof the input images as well as their specially augmented ones, ADCC estimatesthe illuminant with a self-attention DenseNet. The augmented images help totell apart the edge gradients, edge pixels and non-edge ones in log-histogram,which contribute significantly to the feature extraction and color-ambiguityelimination, thereby advancing the accuracy of illuminant estimation.Simulations and experiments on benchmark datasets demonstrate that the proposedalgorithm is effective for illuminant estimation compared to thestate-of-the-art methods. Thus, ADCC offers great potential in promotingapplications of smart cities, such as smart camera, where color is an importantfactor for distinguishing objects.

ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities