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 the Colorization Transformer, a novel approach for diverse high fidelity image
colorization based on self-attention. Given a grayscale image, the colorization proceeds in …

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 …

Image colorization is a classic and important topic in computer graphics, where the aim is to
add color to a monochromatic input image to produce a colorful result. In this survey, we …

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 …

We develop a fully automatic image colorization system. Our approach leverages recent
advances in deep networks, exploiting both low-level and semantic representations. As …

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 …

The colorization of grayscale images is an ill-posed problem, with multiple correct solutions.
In this paper, we propose an adversarial learning colorization approach coupled with …

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 …