PixColor: Pixel Recursive Colorization

We propose a novel approach to automatically produce multiple colorized versions of a grayscale image. Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image. We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image. Then, given the generated low-resolution color image and the original grayscale image as inputs, we train a second CNN to generate a high-resolution colorization of an image. We demonstrate that our approach produces more diverse and plausible colorizations than existing methods, as judged by human raters in a "Visual Turing Test"

Probabilistic Image Colorization

We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset

Two Decades of Colorization and Decolorization for Images and Videos

Colorization is a computer-aided process, which aims to give color to a gray image or video. It can be used to enhance black-and-white images, including black-and-white photos, old-fashioned films, and scientific imaging results. On the contrary, decolorization is to convert a color image or video into a grayscale one. A grayscale image or video refers to an image or video with only brightness information without color information. It is the basis of some downstream image processing applications such as pattern recognition, image segmentation, and image enhancement. Different from image decolorization, video decolorization should not only consider the image contrast preservation in each video frame, but also respect the temporal and spatial consistency between video frames. Researchers were devoted to develop decolorization methods by balancing spatial-temporal consistency and algorithm efficiency. With the prevalance of the digital cameras and mobile phones, image and video colorization and decolorization have been paid more and more attention by researchers. This paper gives an overview of the progress of image and video colorization and decolorization methods in the last two decades.Comment: 12 pages, 19 figure

Automatic Image Colorization

Množstvo rôznych metód bolo navrhnutých na ofarbovanie obrázkov. V mojej bakalárskej práci implementujem automatické kolorizovanie obrázkov pomocou generatívnych kontradiktórnych sietí - GANov. Ukázali sľubné výsledky pri generovaní rôznych dát, vrátane obrázkov. Používam dve modfikácie GANov - DCGAN a CycleGAN. Tieto dve metódy porovnávam a vyhodnocujem pomocou najpoužívanješích metrík, vhodných pre tento problém. V záverečne jčasti práce sú zobrazené aj ukážkové obrázky, vygenerované jednotlivými modelmi.Many different methods have been suggested to colorize images yet. In this thesis, I try to implement a fully automatic image colorization using generative adversarial networks - GANs. They have shown promising results in generating various kinds of data, including images. I adopt two different modifications of GANs - DCGAN and CycleGAN. These two methods are compared, and results are evaluated using the most common metrics used for this problem. Example images are provided as well