Manifold-preserving image colorization with nonlocal estimation

In this paper, we propose a novel scribble-based colorization method which embeds colors using reconstruction weights from nonlocal nearest texture-similar neighbors in image lightness channel. Our main idea is to exploit image textural features to optimize the color distribution with an energy minimization between a pixel and K nonlocal nearest texture-similar neighbors in the feature space. A manifold structure of image lightness channel is embedded into the color channel which best preserves the geometric properties of the original space. Our method is actually the combine of global and local optimum of the cost function, which can be solved with a sparse linear system. Experimental results show that the proposed method can produce higher-quality colorizations than existing methods with sparse constraints, while having better performance in color bleeding. Moreover, simplicity and ease of implementation of our method achieves good runtime performance.