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 …

With the rapid development of cartoon industry, various studies on two-dimensional (2D)
cartoon have been proposed for different application scenarios, such as quality assessment …

Image colorization is the process of estimating RGB colors for grayscale images or video
frames to improve their aesthetic and perceptual quality. Deep learning techniques for …

Scribble colors based line art colorization is a challenging computer vision problem since
neither greyscale values nor semantic information is presented in line arts, and the lack of …

Recently, realistic image generation using deep neural networks has become a hot topic in
machine learning and computer vision. Such an image can be generated at pixel level by …

Sketch or line art colorization is a research field with significant market demand. Different
from photo colorization which strongly relies on texture information, sketch colorization is …

Flat filling is a critical step in digital artistic content creation with the objective of filling line
arts with flat colors. We present a deep learning framework for user-guided line art flat filling …

While many image colorization algorithms have recently shown the capability of producing
plausible color versions from gray-scale photographs, they still suffer from limited semantic …

While many image colorization algorithms have recently shown the capability of producing
plausible color versions from gray-scale photographs, they still suffer from the problems of …

This paper focuses on the colorization problem of near-infrared (NIR) images. Traditional
colorization methods of grayscale images usually depend on users' intervention and cannot …