Example-based image colorization via automatic feature selection and fusion

摘要

Image colorization is an important and difficult problem in image processing with various applications including image stylization and heritage restoration. Most existing image colorization methods utilize feature matching between the reference color image and the target grayscale image. The effectiveness of features is often significantly affected by the characteristics of the local image region. Traditional methods usually combine multiple features to improve the matching performance. However, the same set of features is still applied to the whole images. In this paper, based on the observation that local regions have different characteristics and hence different features may work more effectively, we propose a novel image colorization method using automatic feature selection with the results fused via a Markov Random Field (MRF) model for improved consistency. More specifically, the proposed algorithm automatically classifies image regions as either uniform or non-uniform, and selects a suitable feature vector for each local patch of the target image to determine the colorization results. For this purpose, a descriptor based on luminance deviation is used to estimate the probability of each patch being uniform or non-uniform, and the same descriptor is also used for calculating the label cost of the MRF model to determine which feature vector should be selected for each patch. In addition, the similarity between the luminance of the neighborhood is used as the smoothness cost for the MRF model which enhances the local consistency of the colorization results. Experimental results on a variety of images show that our method outperforms several state-of-the-art algorithms, both visually and quantitatively using standard measures and a user study. (C) 2017 Elsevier B.V. All rights reserved.