Texture-adaptive image colorization framework

In this paper we present how to exploit the textural information to improve scribble-based image colorization. Although many methods have been already proposed for coloring grayscale images based on a set of color scribbles inserted by a user, very few of them take into account textural properties. We demonstrate that the textural information can be extremely helpful for this purpose and it may greatly simplify the colorization process. First, based on a scribbled image we determine the most discriminative textural features using linear discriminant analysis. This makes it possible to boost the initial scribbles by adjoining the regions having similar textural properties. After that, we determine the color propagation paths and compute chrominance of every pixel in the image. For the propagation process we used two competing path cost metrics which are dynamically selected for every scribble. Using these metrics it is possible to efficiently propagate chrominance both over smooth and rough image regions. Texture-based scribble boosting followed by competitive color propagation is the main contribution of the work reported here. Extensive experimental validation documented in this paper demonstrates that image colorization can be substantially improved using the proposed technique.