VISIBLE AND INFRARED IMAGE FUSION USING ENCODER-DECODER NETWORK

Date

2021-01-01

Author

Ataman, Ferhat Can
Akar, Gözde

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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© 2021 IEEE.The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layers together with a loss function using no-reference quality metrics. The analysis is performed qualitatively and quantitatively on various datasets. The results show better performance than state-of-the-art methods. Also, the size of our network enables real-time performance on embedded devices. Project codes can be found at https://github.com/ferhatcan/ pyFusionSR.

Subject Keywords

infrared, visible images, image fusion, deep learning, encoder-decoder network, Deep learning, Encoder-decoder network, Image fusion, Infrared, Visible images

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125570182&origin=inward
https://hdl.handle.net/11511/99032

DOI

https://doi.org/10.1109/icip42928.2021.9506740

Conference Name

2021 IEEE International Conference on Image Processing, ICIP 2021

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

F. C. Ataman and G. Akar, “VISIBLE AND INFRARED IMAGE FUSION USING ENCODER-DECODER NETWORK,” Alaska, Amerika Birleşik Devletleri, 2021, vol. 2021-September, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125570182&origin=inward.