Hand-drawn image colorization based on optimized segmentation

Image colorization is an important topic in the field of computer graphics and image processing. Accurate image segmentation is usually vital for the colorization results in interactive methods. However, in hand-drawn images, complicated lines and patterns as well as discontinuous outlines always make segmentation a hard problem. We propose a method to solve the problem and segment related regions more precisely. Firstly, we use an edge-preserving filter and a Laplace edge detector to smooth color discontinuities and strengthen outlines. Secondly, grayscale energies are computed. Next, stroke energies are calculated according to positions of user strokes to overcome imprecise placements. Finally, grayscale and stroke information are modeled into an energy function, and an optimal segmentation is obtained based on Graph Cut theory. Experimental results show that this method, with more precise segmentation results, can obtain high accurate results for various kinds of hand-drawn images.