We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Computer Science


Publication details for Professor Toby Breckon

Dong, 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.

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.