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...

Colorization describes the digitization and retrospective addition of color to photographic film materials (celluloid nitrate, glass negatives) initially made circulated in a black-and-white format. Revisiting controversial 1980s colorization 24 classic Hollywood studio titles, which incited debate over questions copyright, authorship artistic expression, this essay examines use interpret museu...

Colorization is a very challenging task for computers which requires very high performance of segmentation, object recognition, color understanding, etc., and even for humans, it’s very demanding and arduous work to fully accomplish. To achieve this task, there traditionally has been three approaches: example based model, scribble based model, and data-driven based model which introduced relati...

For realistic and vivid colorization, generative priors have recently been exploited. However, such often fail for in-the-wild complex images due to their limited representation space. In this paper, we propose BigColor, a novel colorization approach that provides diverse with structures. While previous are trained synthesize both image structures colors, learn color prior focus on synthesis gi...