Broad Colorization

The scribble- and example-based colorization methods have fastidious requirements for users, and the training process of deep neural networks for colorization is quite time-consuming. We instead proposed an automatic colorization approach with no dependence on user input and no need to endure long training time, which combines local features and global features of the input gray-scale images. Low-, mid-, and high-level features are united as local features representing cues existed in the gray-scale image. The global feature is regarded as data prior to guiding the colorization process. The local broad learning system is trained for getting the chrominance value of each pixel from the local features, which could be expressed as a chrominance map according to the position of pixels. Then, the global broad learning system is trained to refine the chrominance map. There are no requirements for users in our approach, and the training time of our framework is an order of magnitude faster than the traditional methods based on deep neural networks. To increase the user’s subjective initiative, our system allows users to increase training data without retraining the system. Substantial experimental results have shown that our approach outperforms state-of-the-art methods.