Abstract

Great efforts have been made on illuminant estimation in both academia and industry, leading to the development of various statistical- and learning-based methods. Little attention, however, has been given to images that are dominated by a single color (i.e., pure color images), though they are not trivial to smartphone cameras. In this study, a pure color image dataset, “PolyU Pure Color,” was developed. A lightweight feature-based multilayer perceptron (MLP) neural network model—“Pure Color Constancy (PCC)”—was also developed for estimating the illuminant of pure color images using four color features (i.e., the chromaticities of the maximal, mean, brightest, and darkest pixels) of an image. The proposed PCC method was found to have significantly better performance for pure color images in the PolyU Pure Color dataset and comparable performance for normal images in two existing image datasets, in comparison to the various state-of-the-art learning-based methods, with a good cross-sensor performance. Such good performance was achieved with a much smaller number of parameters (i.e., around 400) and a very short processing time (i.e., around 0.25 ms) for an image using an unoptimized Python package. This makes the proposed method possible for practical deployments.

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