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8 July 2022 Super-resolution network for x-ray security inspection
Haoyuan Du, Meng Fan, Liquan Dong
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Proceedings Volume 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 122810F (2022) https://doi.org/10.1117/12.2616535
Event: 2021 International Conference on Optical Instruments and Technology, 2022, Online Only
Abstract
X-ray imaging is widely used in airports and transportation for security maintaining. Conventional x-ray images often suffer from noise interference, over sharpening or detail loss, especially in areas where multiple objects overlap each other. To overcome the shortcomings of traditional methods, this article presents a method to reveal the details based on convolutional neural network (CNN). We put forward a well-designed super resolution (SR) network exploiting self guided architecture to fuse multi-scale information. At each scale, we adopt residual feature aggregation strategy for extracting representative details. We also find it is beneficial to establish links between high energy (HE) and low energy (LE) images, thus the restored images show more fine textures and better material resolution. The comparison experiments demonstrate that the proposed network outperforms traditional approaches for restoring details and suppressing noise effectively.
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Haoyuan Du, Meng Fan, and Liquan Dong "Super-resolution network for x-ray security inspection", Proc. SPIE 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 122810F (8 July 2022); https://doi.org/10.1117/12.2616535
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KEYWORDS
X-rays
Network security
Super resolution
Inspection
X-ray imaging
Dual energy imaging
Image resolution
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