An infrared image super-resolution imaging algorithm based on auxiliary convolution neural network

Yan Zou; Linfei Zhang; Qian Chen; Bowen Wang; Yan Hu; Yuzhen Zhang

doi:10.1117/12.2581217

7 October 2020 An infrared image super-resolution imaging algorithm based on auxiliary convolution neural network

Yan Zou, Linfei Zhang, Qian Chen, Bowen Wang, Yan Hu, Yuzhen Zhang

Proceedings Volume 11571, Optics Frontier Online 2020: Optics Imaging and Display; 115711B (2020) https://doi.org/10.1117/12.2581217
Event: Optics Frontiers Online 2020: Optics Imaging and Display (OFO-1), 2020, Shanghai, China

Abstract

Convolution neural network has been successfully applied to the super-resolution method of the visible image. In this paper, we propose an infrared image super-resolution imaging algorithm based on auxiliary convolution neural network, which uses the detail information provided by the visible image under low-light conditions for super-resolution imaging of infrared image. In this algorithm, infrared image and visible image are input into the convolution neural network at the same time to obtain high resolution infrared image. The results show that the super-resolution infrared image has more detailed information. Compared with other super-resolution methods, the proposed network can obtain the high super-resolution reconstruction efficiency.

Citation Download Citation

Yan Zou, Linfei Zhang, Qian Chen, Bowen Wang, Yan Hu, and Yuzhen Zhang "An infrared image super-resolution imaging algorithm based on auxiliary convolution neural network", Proc. SPIE 11571, Optics Frontier Online 2020: Optics Imaging and Display, 115711B (7 October 2020); https://doi.org/10.1117/12.2581217

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