Super resolution reconstruction of low light level image based on the feature extraction convolution neural network

Bowen Wang; Yan Zou; Linfei Zhang; Le Li; Chao Zuo

doi:10.1117/12.2586449

12 April 2021 Super resolution reconstruction of low light level image based on the feature extraction convolution neural network

Bowen Wang, Yan Zou, Linfei Zhang, Le Li, Chao Zuo

Author Affiliations +

Proceedings Volume 11731, Computational Imaging VI; 117310E (2021) https://doi.org/10.1117/12.2586449
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

We propose a novel network for low-light-level and visible image fusion enhancement task, which is based on the feature extraction convolution neural network. By extracting the high-frequency information of visible light detector under low illumination, and combining the advantages of wide activation network and channel attention mechanism, the network can automatically filter and extract the useful information in the image to complete the super-resolution reconstruction of low light level image. It makes up for the lack of visible light information and low resolution (LR) of low light level detector at night and can realize all-weather real-time imaging. The experimental results show that our method has better numerical performance than the traditional super-resolution network structure, and also retains more abundant image texture information, which is more in line with the feeling of human eyes.

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Bowen Wang, Yan Zou, Linfei Zhang, Le Li, and Chao Zuo "Super resolution reconstruction of low light level image based on the feature extraction convolution neural network", Proc. SPIE 11731, Computational Imaging VI, 117310E (12 April 2021); https://doi.org/10.1117/12.2586449

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