Real-time user-guided image colorization with learned deep priors
| Autor: | Phillip Isola, Angela S. Lin, Xinyang Geng, Jun-Yan Zhu, Tianhe Yu, Richard Zhang, Alexei A. Efros |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Color histogram Computer science Computer Vision and Pattern Recognition (cs.CV) media_common.quotation_subject Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Grayscale Convolutional neural network Image (mathematics) Computer Science - Graphics Prior probability 0202 electrical engineering electronic engineering information engineering Quality (business) Computer vision media_common business.industry Deep learning 020207 software engineering Computer Graphics and Computer-Aided Design Graphics (cs.GR) Image colorization 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ACM Transactions on Graphics. 36:1-11 |
| ISSN: | 1557-7368 0730-0301 |
| Popis: | We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information, learned from large-scale data. We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer. Our code and models are available at https://richzhang.github.io/ideepcolor. Comment: Accepted to SIGGRAPH 2017. Project page: https://richzhang.github.io/ideepcolor |
| Databáze: | OpenAIRE |
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