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Visible And Infrared Image Fusion Using Encoder-Decoder Neural Network
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FCAtaman_Master_Thesis.pdf
Date
2021-9-07
Author
Ataman, Ferhat Can
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The image fusion aims to gather all important information from the source images into a single image. While the data is reduced, the fusion image has a high spa- tial and spectral resolution. It includes more informative and complete information. In this work, we reviewed state-of-the-art methods in the infrared and visible spec- trum image fusion literature and we present a novel deep learning-based solution. Our proposed method is inspired by encoder-decoder network U-Net architecture [1]. Furthermore, we analyzed the fusion quality measurement metrics. We integrated fu- sion quality measurements into our proposed method’s training step. In this way, we achieved superior performance. The analysis is performed qualitatively and quanti- tatively on TNO [2] and VIFB [3] datasets. The proposed method is compared with state-of-the-art methods and detailed experiments are conducted. It shows the best performance among deep learning-based methods. Project codes can be found at https://github.com/ferhatcan/pyFusionSR.
Subject Keywords
image fusion
,
visible image
,
infrared image
,
encoder-decoder network
URI
https://hdl.handle.net/11511/93208
Collections
Graduate School of Natural and Applied Sciences, Thesis
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F. C. Ataman, “Visible And Infrared Image Fusion Using Encoder-Decoder Neural Network,” M.S. - Master of Science, Middle East Technical University, 2021.
