# Image Colorization Using a Deep Convolutional Neural Network Source: [Image Colorization Using a Deep Convolutional Neural Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:20:33.540Z Authors: Tung Nguyen, Kazuki Mori, Ruck Thawonmas Published: 2016-04-27T02:16:43.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()