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Infrared Image Colorization Using S-Shape Network

Dong, Z.; Kamata, S.; Breckon, T.P.

Infrared Image Colorization Using S-Shape Network Thumbnail


Z. Dong

S. Kamata


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.


Dong, Z., Kamata, S., & Breckon, T. (2018). Infrared Image Colorization Using S-Shape Network. In Proc. Int. Conf. on Image Processing (2242-2246).

Conference Name 25th IEEE International Conference on Image Processing (ICIP).
Conference Location Athens, Greece
Start Date Oct 7, 2018
End Date Oct 10, 2018
Acceptance Date May 4, 2018
Online Publication Date Sep 6, 2018
Publication Date 2018
Deposit Date Jun 10, 2018
Publicly Available Date Jun 11, 2018
Pages 2242-2246
Series ISSN 2381-8549
Book Title Proc. Int. Conf. on Image Processing
ISBN 9781479970629
Keywords deep learning, image colorization, near infrared, false colour
Public URL


Accepted Conference Proceeding (1.3 Mb)

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