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Color vision deficiency affects 8% men and one in every 200 women. There are many different types of color blindness with the red-green as the most common. Most models for color correction are based on physiological models of how people with color vision deficiency perceive the world, with the goal of reducing errors derived from the color blindness simulation formula. In this paper we present Deep Correct, a novel Deep Learning based method for color correcting images in order to improve accessibility for people with color vision deficiency. The key elements of this work with regard to color blindness are two-fold: 1) we propose a data-driven Deep Learning approach for color correction and 2) we create an objective, quantitative metric for determining the distinguishability of images. Additionally, as a more general Deep Learning contribution, we propose a new method of training neural networks by utilizing error gradients from pretrained networks in order to train new, smaller networks.
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