基于深度学习的红外夜视图像超分辨率重建
王丹, 陈亮. 基于深度学习的红外夜视图像超分辨率重建[J]. 红外技术, 2019, 41(10): 963.
WANG Dan, CHEN Liang. Super-resolution Reconstruction of Infrared Images in Night Environments Based on Deep-learning[J]. Infrared Technology, 2019, 41(10): 963.
[1] Gyaourova A, Bebis G, Pavlidis I. Fusion of infrared and visible images for face recognition computer vision[C]//ECCV 2004, Springer Berlin Heidelberg, 2004: 456-468.
[2] MA J , CHEN C , LI C , et al. Infrared and visible image fusion via gradient transfer and total variation minimization[J]. Information Fusion, 2016, 31:100-109.
[3] Bavirisetti D P, Dhuli R. Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-loeve transform[J]. IEEE Sensors Journal, 2015, 16(1): 203-209.
[4] ZHAO Y, CHEN Q, SUI X, et al. A novel infrared image super-resolution method based on sparse representation[J]. Infrared Physics & Technology, 2015, 71: 506-513.
[5] DONG C, CHEN C L, HE K, et al. Learning a deep convolutional network for image super -resolution[C]//Proceedings of the 2014 European Conference on Computer Vision, 2014, 8692: 184-199.
[6] YANG J, Wright J, HUANG T, et al. Image super-resolution as sparse representation of raw image patches[C]// Computer Vision and Pattern Recognition, 2008 CVPR of IEEE Conference, 2008: 1-8.
[7] DONG C, CHEN C L, TANG X. Accelerating the super-resolution convolutional neural network[C]//Proceedings of the 2016 European Conference on Computer Vision, 2016: 391-407.
[8] Ma Wenye, Osher S. A TV Bregman iterative model of Retinex theory[J]. Inverse Problems & Imaging, 2012, 6(4): 697-708.
[9] SHI W, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]// IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1874-1883.
[10] Ganin Y , Ustinova E , Ajakan H , et al. Domain-adversarial training of neural networks[J]. Journal of Machine Learning Research, 2015, 17(1): 2096-2030.
[11] Yosinski J, Clune J, Bengio Y, et al. How transferable are features in deep neural networks[J]. Eprint Arxiv, 2014, 27: 3320-3328.
[12] Mittal A, Moorthy A K, Bovik A C. Blind/referenceless image spatial quality evaluator[C]// Signals, Systems and Computers of IEEE Conference, 2012: 723-727.
王丹, 陈亮. 基于深度学习的红外夜视图像超分辨率重建[J]. 红外技术, 2019, 41(10): 963. WANG Dan, CHEN Liang. Super-resolution Reconstruction of Infrared Images in Night Environments Based on Deep-learning[J]. Infrared Technology, 2019, 41(10): 963.
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