Huang Siyuan, Liu Limin, Dong Jian, et al. Review of ground filtering algorithms for vehicle LiDAR scans point cloud dataReview of ground filtering algorithms for vehicle LiDAR scans point cloud data[J]. Opto-Electronic Engineering, 2020, 47(12): 190688.
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Opto-Electronic Engineering
ISSN: 1003-501X
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
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.
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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review | TN249
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 190688
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Article | TN391
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 190636
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Zhang Baohua, Zhu Siyu, Lv Xiaoqi, et al. Soft multilabel learning and deep feature fusion for unsupervised person re-identification[J]. Opto-Electronic Engineering, 2020, 47(12): 190636.
Article | TP391.41
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 190669
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Wang Ronggui, Wang Jing, Yang Juan, et al. Feature pyramid random fusion network for visible-infrared modality person re-identification[J]. Opto-Electronic Engineering, 2020, 47(12): 190669.
Article | TN391.4
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 200002
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Liu Xia, Gan Quan, Li Bing, et al. Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest[J]. Opto-Electronic Engineering, 2020, 47(12): 200002.
Article | TN391
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 200006
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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.
Article | TP391.4
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 200007
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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.
Article | TP391.4
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 200036
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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.
Article | TN29
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 200067
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Zhang Xinli, Tang Qunzhi, Wu Haocheng, et al. Moisture-proof seal optical fiber connector[J]. Opto-Electronic Engineering, 2020, 47(12): 200067.
Article | TN29
Online Time:Dec 22, 2020
Opto-Electronic Engineering, Vol. 47, Issue 12, pp. 200111
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Long Xiao, Bao Hua, Rao Changhui, et al. Improved fast phase unwrapping algorithm based on parallel acceleration[J]. Opto-Electronic Engineering, 2020, 47(12): 200111.