Terahertz image super-resolution using an improved attention U-net

Le Li; Yan Zou; Bowen Wang; Linfei Zhang; Yuzhen Zhang

doi:10.1117/12.2590786

12 April 2021 Terahertz image super-resolution using an improved attention U-net

Le Li, Yan Zou, Bowen Wang, Linfei Zhang, Yuzhen Zhang

Proceedings Volume 11731, Computational Imaging VI; 117310D (2021) https://doi.org/10.1117/12.2590786
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

Terahertz waves refer to electromagnetic waves with frequencies ranging from 0.1THz to 10THz.Due to the ability to penetrate many non-polar materials, terahertz waves can be used to detect hidden objects. A convolutional neural network structure called Attention U-Net to achieve super-resolution of terahertz images is proposed in this paper. The function of the convolutional layer and pooling layer in the encoding path is reducing the size and extracting the edge features of the image, while the role of the deconvolution layer in the decoding path is to up-sample the image and restore the image content. The introduction of skip connection on the feature map of the symmetrical encoding path and decoding path maximizes the utilization of feature information in each layer of the network and effectively solves the problem of gradient disappearance. This network also replaces the convolution on the codec path with the attention mechanism block, including the spatial attention mechanism and the channel attention mechanism, which makes the extracted features more directional, obtains more detailed information about the target that needs to be focused and suppresses other Useless information. The network and algorithm proposed in this paper have good results in experiments and have a wide range of application prospects in the field of security inspection.

Citation Download Citation

Le Li, Yan Zou, Bowen Wang, Linfei Zhang, and Yuzhen Zhang "Terahertz image super-resolution using an improved attention U-net", Proc. SPIE 11731, Computational Imaging VI, 117310D (12 April 2021); https://doi.org/10.1117/12.2590786

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