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Infrared Image Colorization Using a S-shape Network
![](https://sigport.org/sites/default/files/styles/home/public/ICIP_2018_2_0.jpg?itok=3Zvo4ksZ)
- Citation Author(s):
- Submitted by:
- Dong Ziyue
- Last updated:
- 5 October 2018 - 2:52am
- Document Type:
- Poster
- Document Year:
- 2018
- Event:
- Presenters:
- Ziyue Dong
- Paper Code:
- ICIP-1575
- Categories:
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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.