Image Colorization Using a Deep Convolutional Neural Network
In this paper, we present a novel approach that uses deep learning techniques for colorizing grayscale images. By utilizing a pre-trained convolutional neural network, which is originally designed for image classification, we are able to separate content and style of different images and recombine them into a single image. We then propose a method that can add colors to a grayscale image by combining its content with style of a color image having semantic similarity with the grayscale one. As an application, to our knowledge the first of its kind, we use the proposed method to colorize images of ukiyo-e a genre of Japanese painting?and obtain interesting results, showing the potential of this method in the growing field of computer assisted art.
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ColorizationGeneral Classificationimage-classificationImage ClassificationImage ColorizationSemantic SimilaritySemantic Textual SimilaritySimilar Papers 제목 키워드 기반
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