In this paper a novel method for machine-based black and white films colorization is presented. The kernel of the proposed scheme is a trained artificial neural network which maps the frame pixels from a grayscale space into a color space. We employ the texture coding method to capture the line/texture characteristics of each pixel as its most significant gray scale space feature, and using tha...

We propose in this paper an automated colorization of segmented images based on a color harmony rule defined by Itten called the contrast of proportion. Artists create an image drawing a sketch and filling its regions following harmony rules. The most significant regions are generally filled first because of their meaning. Then the other regions are filled depending on the previous colors. Our ...

Automatic image colorization consists in adding colors to a new greyscale image without any user intervention. The problem, stated like this, is ill-posed, in the sense that one cannot guess the colors to assign to a greyscale image without any prior knowledge. Indeed, many objects can have different colors: not

By embedding images as surfaces in a high dimensional coordinate space defined by each pixel’s Cartesian coordinates and color values, we directly define and employ cotangent-based, discrete differentialgeometry operators. These operators define discrete energies useful for image segmentation and colorization.

Colorizing a grayscale image is inherently an ill-posed problem with multi-modal uncertainty. Language-based colorization offers natural way of interaction to reduce such uncertainty via user-provided caption. However, the color-object coupling and mismatch issues make mapping from word color difficult. In this paper, we propose L-CoDe, Colorization network using Decoupled conditions. A predict...