Fast Image Colorization Based on Local and Global Consistency

Image colorization refers to the process of semiautomatic adding colors to a grayscale image. A novel image colorization approach based on local and global consistency is proposed, which applies the graph-based semi-supervised learning theory to solve the problem of colorization. Firstly, constructing a graph model according to the similarities between pixels, and then determine a quadratic cost function with sufficient smoothness which can fully guarantee the image structure information, finally the colors are propagated to the un-colored pixels from colored pixels by minimizing the cost function. In order to meet the time requirements when dealing with high resolution images, the authors further propose a segmentation-based approach, instead of directly on pixels, which regards image segmented region as the basic unit during colors propagating. Experiments on different natural images demonstrate that our approach's computational complexity is significantly reduced while achieves high quality colorization results compared with two existing techniques.