A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 9
Issue 9
IEEE/CAA Journal of Automatica Sinica
| Citation: | Y. Ma, X. Y. Wang, W. J. Gao, Y. Du, J. Huang, and F. Fan, “Progressive fusion network based on infrared light field equipment for infrared image enhancement,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1687–1690, Sept. 2022. doi: 10.1109/JAS.2022.105812
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J. Ma, Y. Ma, and C. Li, “Infrared and visible image fusion methods and applications: A survey,” Inf. Fusion, vol. 45, pp. 153–178, 2019. doi: 10.1016/j.inffus.2018.02.004
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J. A. Sobrino, F. Del Frate, M. Drusch, J. C. Jiménez-Muñoz, P. Manunta, and A. Regan, “Review of thermal infrared applications and requirements for future high-resolution sensors,” IEEE Trans. Geoscience and Remote Sensing, vol. 54, no. 5, pp. 2963–2972, 2016. doi: 10.1109/TGRS.2015.2509179
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Y. Ma, Z. Wang, H. Yang, and L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: A survey,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 315–329, 2020. doi: 10.1109/JAS.2020.1003021
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C.-C. Hsieh, C.-Y. Wu, F.-W. Jih, and T.-P. Sun, “Focal-plane-arrays and CMOS readout techniques of infrared imaging systems,” IEEE Trans. Circ. Syst. Video Technol., vol. 7, no. 4, pp. 594–605, 1997. doi: 10.1109/76.611171
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J. Liang, W. Hu, Z. Ye, L. Liao, Z. Li, X. Chen, and W. Lu, “Improved performance of HgCdTe infrared detector focal plane arrays by modulating light field based on photonic crystal structure,” J. Applied Physics, vol. 115, no. 18, p. 184504, 2014.
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J. Ma, W. Gao, Y. Ma, J. Huang, and F. Fan, “Learning spatialparallax prior based on array thermal camera for infrared image enhancement,” IEEE Trans. Ind. Informat. DOI: 10.1109/TII.2021.3118682.
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Y. Choi, N. Kim, S. Hwang, and I. S. Kweon, “Thermal image enhancement using convolutional neural network,” in Proc. IEEE Int. Conf. Intelligent Robots and Systems, 2016, pp. 223–230.
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V. Chudasama, H. Patel, K. Prajapati, K. P. Upla, R. Ramachandra, K. Raja, and C. Busch, “TherisurNet−A computationally efficient thermal image super-resolution network,” in Proc. CVPRW, 2020, pp. 86–87.
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E. Mandanici, L. Tavasci, F. Corsini, and S. Gandolfi, “A multiimage super-resolution algorithm applied to thermal imagery,” Applied Geomatics, vol. 11, no. 3, pp. 215–228, 2019. doi: 10.1007/s12518-019-00253-y
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P. Cascarano, F. Corsini, S. Gandolfi, E. L. Piccolomini, E. Mandanici, L. Tavasci, and F. Zama, “Super-resolution of thermal images using an automatic total variation based method,” Remote Sensing, vol. 12, no. 10, p. 1642, 2020.
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S. Wang, T. Zhou, Y. Lu, and H. Di, “Detail-preserving transformer for light field image super-resolution,” in Proc. AAAI, 2022, pp. 2522–2530.
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J. Jin, J. Hou, J. Chen, and S. Kwong, “Light field spatial superresolution via deep combinatorial geometry embedding and structural consistency regularization,” in Proc. CVPR, 2020, pp. 2260–2269.
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S. Zhang, Y. Lin, and H. Sheng, “Residual networks for light field image super-resolution,” in Proc. CVPR, 2019, pp. 11046–11055.
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P. Yi, Z. Wang, K. Jiang, J. Jiang, and J. Ma, “Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations,” in Proc. ICCV, 2019, pp. 3106–3115.
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X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” in Proc. CVPR, 2019, pp. 9308–9316.
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J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proc. ICCV, 2017, pp. 764–773.
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S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M.-H. Yang, and L. Shao, “Learning enriched features for real image restoration and enhancement,” in Proc. Europ. Conf. Comput. Vis., 2020, pp. 492–511.
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