Colorization is a process of converting grayscale images into visually acceptable color
images. The main goal is to convince the viewer of the authenticity of the result. Grayscale …

This paper tackles the automatic colorization task of a sketch image given an already-
colored reference image. Colorizing a sketch image is in high demand in comics, animation …

We use large amounts of unlabeled video to learn models for visual tracking without manual
human supervision. We leverage the natural temporal coherency of color to create a model …

Given a grayscale photograph as input, this paper attacks the problem of hallucinating a
plausible color version of the photograph. This problem is clearly underconstrained, so …

We propose a deep learning approach for user-guided image colorization. The system
directly maps a grayscale image, along with sparse, local user" hints" to an output …

We present a novel technique to automatically colorize grayscale images that combines
both global priors and local image features. Based on Convolutional Neural Networks, our …

We propose the first deep learning approach for exemplar-based local colorization. Given a
reference color image, our convolutional neural network directly maps a grayscale image to …

Colorization has attracted increasing interest in recent years. Classic reference-based
methods usually rely on external color images for plausible results. A large image database …

This paper investigates into the colorization problem which converts a grayscale image to a
colorful version. This is a very difficult problem and normally requires manual adjustment to …

This paper presents the first end-to-end network for exemplar-based video colorization. The
main challenge is to achieve temporal consistency while remaining faithful to the reference …