Opto-Electronic Engineering
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Vol. 47, No. 12, 2020
Cover Story:Zhao Y Y, Shi S X, et al. Light-field image super-resolution based on multi-scale feature fusion[J]. Opto-Electronic Engineering, 2020, 47(12): 200007

As a new generation of imaging equipment, a light-field camera can simultaneously capture the spatial position and incident angle of light rays. However, the recorded light-field has a trade-off between spatial resolution and angular resolution. Therefore, a light-field super-resolution network that fuses multi-scale features to obtain super-resolved light-field is proposed in this paper. The deep-learning-based network framework contains three major modules: multi-scale feature extraction module, global feature fusion module, and up-sampling module.The network proposed in this paper was applied to the synthetic light-field dataset and the real-world light-field dataset for light-field images super-resolution. The experimental results on the synthetic light-field dataset and real-world light-field dataset showed that this method outperforms other state-of-the-art methods in both visual and numerical evaluations.
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Ruan Yong, Xu Tianrong, Yang Tao, et al. Position-rate control for the time delay control system of tip-tilt mirror[J]. Opto-Electronic Engineering, 2020, 47(12): 200006. 
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Online Time:Dec 22, 2020
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Zhao Yuanyuan, Shi Shengxian. Light-field image super-resolution based on multi-scale feature fusion[J]. Opto-Electronic Engineering, 2020, 47(12): 200007. 
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Online Time:Dec 22, 2020
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Chen Hanshen, Yao Minghai, Qu Xinyu. Pavement crack detection based on the U-shaped fully convolutional neural network[J]. Opto-Electronic Engineering, 2020, 47(12): 200036. 
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