Laser Journal, Volume. 45, Issue 4, 114(2024)
Super-resolution reconstruction of remote sensing images based on light weight generative adversarial network
[1] [1] Tian Y, Jia R S, Xu S H, et al. Super-resolution reconstruction of remote sensing images based on convolutional neural network[J]. Journal of Applied Remote Sensing, 2019, 13(4): 046502-046502.
[2] [2] Wang Y, Li H, Jia P, et al. Multi-scale densenets-based aircraft detection from remote sensing images[J]. Sensors, 2019, 19(23): 5270-5288.
[3] [3] Feng X, Su X, Shen J, et al. Single space object image denoising and super - resolution reconstructing using deep convolutional networks[J]. Remote Sensing, 2019, 11(16): 1910.
[4] [4] Feng X, Su X, Xu Z, et al. Single space object image super resolution reconstructing using convolutional networks in wavelet transform domain[C]//2020 IEEE 3rd International Conference on Electronics Technology (ICET). IEEE, 2020: 862-866.
[5] [5] Meijering E, Unser M. A note on cubic convolution interpolation[J]. IEEE Transactions on Image processing, 2003, 12(4): 477-479.
[6] [6] Niu B, Wen W, Ren W, et al. Single image super-resolution via a holistic attention network[C]//Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XII 16. Springer International Publishing, 2020: 191-207.
[9] [9] Dong C, Loy C C, He K, et al. Learning a deep convolutional network for image super-resolution[C]//Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13. Springer International Publishing, 2014: 184-199.
[10] [10] Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network[C]//Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. Springer International Publishing, 2016: 391-407.
[11] [11] 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]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1874-1883.
[12] [12] Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681-4690.
[13] [13] Wang X, Yu K, Wu S, et al. Esrgan: Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European conference on computer vision (ECCV) workshops. 2018: 0-0.
[14] [14] Lei S, Shi Z, Zou Z. Super-resolution for remote sensing images via local-global combined network[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1243-1247.
[15] [15] Xu W, Xu G, Wang Y, et al. Deep memory connected neural network for optical remote sensing image restoration[J]. Remote Sensing, 2018, 10(12): 1893.
[16] [16] Dong X, Sun X, Jia X, et al. Remote sensing image super-resolution using novel dense - sampling networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(2): 1618-1633.
[17] [17] Wang X, Wu Y, Ming Y, et al. Remote sensing imagery super resolution based on adaptive multi-scale feature fusion network[J]. Sensors, 2020, 20(4): 1142.
[18] [18] Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 13713-13722.
[19] [19] Yang Y, Newsam S. Bag-of-visual-words and spatial extensions for land-use classification[C]//Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems. 2010: 270-279.
[20] [20] Cheng G, Han J, Lu X. Remote sensing image scene classification: Benchmark and state of the art[J]. Proceedings of the IEEE, 2017, 105(10): 1865-1883.
[21] [21] Hore A, Ziou D. Image quality metrics: PSNR vs. SSIM[C]//2010 20th international conference on pattern recognition. IEEE, 2010: 2366-2369.
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ZHANG Pengying, ZHANG Ming, LI Jianjun, ZHANG Baohua. Super-resolution reconstruction of remote sensing images based on light weight generative adversarial network[J]. Laser Journal, 2024, 45(4): 114
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Received: Sep. 11, 2023
Accepted: Nov. 26, 2024
Published Online: Nov. 26, 2024
The Author Email: Ming ZHANG (nkd_zm@imust.edu.cn)