SEDRFuse: A Symmetric Encoder-Decoder With Residual Block Network for Infrared and Visible Image Fusion

Image fusion is an important task for computer vision as a diverse range of applications are benefiting from the fusion operation. The existing image fusion methods are largely implemented at the pixel level, which may introduce artifacts and/or inconsistencies, while the computational complexity is...

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Vydáno v:IEEE transactions on instrumentation and measurement Ročník 70; s. 1 - 15
Hlavní autoři: Jian, Lihua, Yang, Xiaomin, Liu, Zheng, Jeon, Gwanggil, Gao, Mingliang, Chisholm, David
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Attention map
Coders
Compensation
compensation feature
Computer vision
encoder–decoder
Feature extraction
Image fusion
Image processing
Image reconstruction
Infrared imagery
Machine learning
Night vision
Principal component analysis
residual block
Task analysis
Transforms
ISSN:0018-9456, 1557-9662
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Shrnutí:Image fusion is an important task for computer vision as a diverse range of applications are benefiting from the fusion operation. The existing image fusion methods are largely implemented at the pixel level, which may introduce artifacts and/or inconsistencies, while the computational complexity is relatively high. In this article, we propose a symmetric encoder-decoder with residual block (SEDRFuse) network to fuse infrared and visible images for night vision applications. At the training stage, the SEDRFuse network is trained to create a fixed feature extractor. At the fusing stage, the trained extractor is utilized to extract the intermediate and compensation features, which are generated by the residual block and the first two convolutional layers from the input source images, respectively. Two attention maps, which are derived from the intermediate features, are then multiplied by the intermediate features for fusion. The salient compensation features obtained through elementwise selection are passed to the corresponding deconvolutional layers for processing. Finally, the fused intermediate features and the selected compensation features are decoded to reconstruct the fused image. Experimental results demonstrate that the proposed fusion solution, i.e., SEDRFuse, outperforms the state-of-the-art fusion methods in terms of both subjective and objective evaluations.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.3022438