Publication details for Professor Toby BreckonDong, Z., Kamata, S. & Breckon, T.P. (2018), Infrared Image Colorization Using S-Shape Network, 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece, IEEE, Piscataway, 2242-2246.
- Publication type: Conference Paper
- ISSN/ISBN: 9781479970629, 9781479970612, 2381-8549
- DOI: 10.1109/cip.2018.8451230
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
This paper proposes a novel approach for colorizing near infrared (NIR) images using a S-shape network (SNet). The proposed approach is based on the usage of an encoder-decoder architecture followed with a secondary assistant network. The encoder-decoder consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. The assistant network is a shallow encoder-decoder to enhance the edge and improve the output, which can be trained end-to-end from a few image examples. The trained model does not require any user guidance or a reference image database. Furthermore, our architecture will preserve clear edges within NIR images. Our overall architecture is trained and evaluated on a real-world dataset containing a significant amount of road scene images. This dataset was captured by a NIR camera and a corresponding RGB camera to facilitate side-by-side comparison. In the experiments, we demonstrate that our SNet works well, and outperforms contemporary state-of-the-art approaches.