![]() Convert all training images from the RGB color space to the Lab color space.The entire (simplified) process can be summarized as: Given the input L channel and the predicted ab channels we can then form our final output image. Since the L channel encodes only the intensity, we can use the L channel as our grayscale input to the network.įrom there the network must learn to predict the a and b channels. The L channel encodes lightness intensity onlyĪ full review of the Lab color space is outside the scope of this post (see this guide for more information on Lab), but the gist here is that Lab does a better job representing how humans see color.But unlike the RGB color space, Lab encodes color information differently: Similar to the RGB color space, the Lab color space has three channels. started with the ImageNet dataset and converted all images from the RGB color space to the Lab color space. decided to attack the problem of image colorization by using Convolutional Neural Networks to “hallucinate” what an input grayscale image would look like when colorized. Previous approaches to black and white image colorization relied on manual human annotation and often produced desaturated results that were not “believable” as true colorizations. The technique we’ll be covering here today is from Zhang et al.’s 2016 ECCV paper, Colorful Image Colorization. ![]() How can we colorize black and white images with deep learning? Figure 1: Zhang et al.’s architecture for colorization of black and white images with deep learning. ![]() We’ll then explore some examples and demos of our work. In the first part of this tutorial, we’ll discuss how deep learning can be utilized to colorize black and white images.įrom there we’ll utilize OpenCV to colorize black and white images for both: Looking for the source code to this post? Jump Right To The Downloads Section Black and white image colorization with OpenCV and Deep Learning
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