References
- Murshed H,Wei Z,Ahmed M . Perfect Single Image (SR)Super-Resolutionwith,Deep Super Resolution Convolutional Neural NetworkandOpenCV Method [J]. IOSR journal of computer engineering, 2020(3):22.
- Ward C M, Harguess J, Crabb. Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)[C]//Applications of Digital Image Processing XL. SPIE, 2017, 10396: 19-30.
- Wang Lie, Yin Jin-wei. Small Object Detection Method Based on SRCNN and SSD network [J].Computer simulation, 2020, 37(3):5.
- Shen H F, Li P X, Zhang L P. Overview of image super-resolution reconstruction techniques and methods [J]. Optical Technology, 2009(2):7.
- Pu Jian, Zhang Junping, Huang Hua. Review of super-resolution algorithms [J]. Journal of Shandong University: Engineering Science Edition, 2009(1):6.
- ANWARS, KHANS, BARNESN. A deep journey into super-resolution: a survey[J]. ACM Computing Surveys (CSUR), 2020, 53(3):1–34.
- Kim J, Lee J K, Lee K M. Accurate Image Super-Resolution Using Very Deep Convolutional Networks[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2016
- Wang Jiaming, LU Tao. Satellite image super-resolution algorithm based on multi-scale residual deep neural network [J]. Journal of Wuhan Institute of Technology, 2018, 40(04):440–445.
- Wan Xuefen, Cui Jian, WANG Guanjun. Research on Image Super-resolution Reconstruction Processing Algorithm[C]// National Conference on Optoelectronics and Quantum Electronics Technology. Chinese Institute of Electronics, 2011.
- TIAN Yan, TIAN Jinwen, LIU Jian. Implementation for Super Resolution--An Improved Image Interpolation Based on Wavelet Implementation of Super-Resolution Technology -- An Improved Wavelet Interpolation Method [J]. Journal of Image and Graphics, 2003, 45(12):1422–1426.
- Jiang Hao, Wang Bofu, Zhuang Qiliang. Reconstruction of turbulent flow field based on super-resolution reconstruction method [J]. Experimental Fluid Mechanics, 2022(036-003).
- Wang Rong, Zhang Yonghui, Zhang Jian. Image supe-resolution Reconstruction Method based on CNN [J]. Computer Engineering and Design, 2019, 40(6):6.
- Zhong Z, Chen Y, Hou S. Super-resolution reconstruction method of infrared images of composite insulators with abnormal heating based on improved SRGAN [J]. IET generation, transmission & distribution, 2022(10):16.
- Zou Penghui, Zeng Yijie, Duan Zhenghong. Research on image super-resolution Reconstruction based on SRGAN technology [J]. Science and Technology Trends, 2019(18):1.
- Liu Yiwen. Research on Low resolution face Detection Algorithm Based on Deep Learning [D]. University of Electronic Science and Technology of China, 2019.
- Hu Lei, Wang Zugen, Chen Tian, et al. An Improved super-resolution Reconstruction algorithm for SRGAN infrared Image [J]. Journal of System Simulation, 2021(033-009).
- Nagano Y, Kikuta Y. SRGAN for super-resolving low-resolution food images[C]//Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management. 2018: 33-37.
- Li J, Wu L, Wang S. Super resolution image reconstruction of textile based on SRGAN[C]//2019 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2019: 436-439.
- Wang X, Yu K, Wu S. Esrgan: Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European conference on computer vision (ECCV) workshops. 2018: 0-0.
- Wang X, Xie L, Dong C. Real-esrgan: Training real-world blind super-resolution with pure synthetic data[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 1905-1914.
- Menon S, Damian A, Hu S. Pulse: Self-supervised photo upsampling via latent space exploration of generative models[C]//Proceedings of the ieee/cvf conference on computer vision and pattern recognition. 2020: 2437-2445.