References(36)
[1]
L. Zhang and X. Wu, An edge-guided image interpolation algorithm via directional filtering and data fusion, IEEE Trans. Image Process., vol. 15, no. 8, pp. 2226–2238, 2006.
[2]
K. Zhang, X. Gao, D. Tao, and X. Li, Single image super-resolution with non-local means and steering kernel regression, IEEE Trans. Image Process., vol. 21, no. 11, pp. 4544–4556, 2012.
[3]
R. Timofte, V. De, and L. Van Gool, Anchored neighborhood regression for fast example-based super-resolution, in Proc. 2013 IEEE Int. Conf. Computer Vision, Sydney, Australia, 2013, pp. 1920–1927.
[4]
W. He, N. Yokoya, L. Yuan, and Q. Zhao, Remote sensing image reconstruction using tensor ring completion and total variation, IEEE Trans. Geosci. Remote Sens., vol. 57, no. 11, pp. 8998–9009, 2019.
[5]
Z. Shao, L. Wang, Z. Wang, and J. Deng, Remote sensing image super-resolution using sparse representation and coupled sparse autoencoder, IEEE J. Selected Topics Appl. Earth Obs. Remote Sens., vol. 12, no. 8, pp. 2663–2674, 2019.
[6]
K. Jiang, Z. Wang, P. Yi, G. Wang, T. Lu, and J. Jiang, Edge-enhanced GAN for remote sensing image superresolution, IEEE Trans. Geosci. Remote Sens., vol. 57, no. 8, pp. 5799–5812, 2019.
[7]
W. Xu, G. Xu, Y. Wang, X. Sun, D. Lin, and Y. Wu, High quality remote sensing image super-resolution using deep memory connected network, in Proc. 2018 IEEE Int. Geoscience and Remote Sensing Symp., Valencia, Spain, 2018, pp. 8889–8892.
[8]
C. Dong, C. C. Loy, K. He, and X. Tang, Learning a deep convolutional network for image super-resolution, in Proc. 13th European Conf. Computer Vision, Zurich, Switzerland, 2014, pp. 184–199.
[9]
T. Tong, G. Li, X. Liu, and Q. Gao, Image super-resolution using dense skip connections, in Proc. 2017 IEEE Int. Conf. Computer Vision, Venice, Italy, 2017, pp. 4809–4817.
[10]
J. Kim, J. K. Lee, and K. M. Lee, Deeply-recursive convolutional network for image super-resolution, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 1637–1645.
[11]
S. Liu, R. Gang, C. Li, and R. Song, Adaptive deep residual network for single image super-resolution, Comp. Visual Media, vol. 5, no. 4, pp. 391–401, 2019.
[12]
M. Shen, P. Yu, R. Wang, J. Yang, and L. Xue, Image super-resolution via multi-path recursive convolutional network, (in Chinese), Opto-Electron. Eng., vol. 46, no. 11, p. 180489, 2019.
[13]
S. Lei, Z. Shi, and Z. Zou, Super-resolution for remote sensing images via local–global combined network, IEEE Geosci. Remote Sens. Lett., vol. 14, no. 8, pp. 1243–1247, 2017.
[14]
X. Dong, X. Sun, X. Jia, Z. Xi, L. Gao, and B. Zhang, Remote sensing image super-resolution using novel dense-sampling networks, IEEE Trans. Geosci. Remote Sens., vol. 59, no. 2, pp. 1618–1633, 2021.
[15]
Z. Pan, W. Ma, J. Guo, and B. Lei, Super-resolution of single remote sensing image based on residual dense backprojection networks, IEEE Trans. Geosci. Remote Sens., vol. 57, no. 10, pp. 7918–7933, 2019.
[16]
X. Dong, L. Wang, X. Sun, X. Jia, L. Gao, and B. Zhang, Remote sensing image super-resolution using second-order multi-scale networks, IEEE Trans. Geosci. Remote Sens., vol. 59, no. 4, pp. 3473–3485, 2021.
[17]
S. Wang, T. Zhou, Y. Lu, and H. Di, Contextual transformation network for lightweight remote-sensing image super-resolution, IEEE Trans. Geosci. Remote Sens., vol. 60, p. 5615313, 2022.
[18]
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 1874–1883.
[19]
J. Kim, J. K. Lee, and K. M. Lee, Accurate image super-resolution using very deep convolutional networks, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 1646–1654.
[20]
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, Enhanced deep residual networks for single image super-resolution, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 2017, pp. 1132–1140.
[21]
Z. Hui, X. Wang, and X. Gao, Fast and accurate single image super-resolution via information distillation network, in Proc. 2018 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 723–731.
[22]
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, Residual dense network for image super-resolution, in Proc. 2018 IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 2472–2481.
[23]
Y. Qiu, R. Wang, D. Tao, and J. Cheng, Embedded block residual network: A recursive restoration model for single-image super-resolution, in Proc. 2019 IEEE/CVF Int. Conf. Computer Vision (ICCV), Seoul, Republic of Korea, 2019, pp. 4179–4188.
[24]
J. Liu, W. Zhang, Y. Tang, J. Tang, and G. Wu, Residual feature aggregation network for image super-resolution, in Proc. 2020 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 2356–2365.
[25]
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, Squeeze-and-excitation networks, IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 8, pp. 2011–2023, 2020.
[26]
S. Woo, J. Park, J. Y. Lee, and I. S. Kweon, CBAM: Convolutional block attention module, in Proc. 15th European Conf. Computer Vision, Munich, Germany, 2018, pp. 3–19.
[27]
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, Image super-resolution using very deep residual channel attention networks, in Proc. 15th European Conf. Computer Vision, Munich, Germany, 2018, pp. 294–310.
[28]
T. Dai, J. Cai, Y. Zhang, S. T. Xia, and L. Zhang, Second-order attention network for single image super-resolution, in Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 11057–11066.
[29]
Z. Hui, X. Gao, Y. Yang, and X. Wang, Lightweight image super-resolution with information multi-distillation network, in Proc. 27th ACM Int. Conf. Multimedia, Nice, France, 2019, pp. 2024–2032.
[30]
L. Courtrai, M. T. Pham, C. Friguet, and S. Lefèvre, Small object detection from remote sensing images with the help of object-focused super-resolution using wasserstein GANs, in Proc. 2020 IEEE Int. Geoscience and Remote Sensing Symp., Waikoloa, HI, USA, 2020, pp. 260–263.
[31]
A. Ma, J. Wang, Y. Zhong, and Z. Zheng, FactSeg: Foreground activation-driven small object semantic segmentation in large-scale remote sensing imagery, IEEE Trans. Geosci. Remote Sens., vol. 60, p. 5606216, 2021.
[32]
D. Zhang, J. Shao, X. Li, and H. T. Shen, Remote sensing image super-resolution via mixed high-order attention network, IEEE Trans. Geosci. Remote Sens., vol. 59, no. 6, pp. 5183–5196, 2021.
[33]
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, Pyramid scene parsing network, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 6230–6239.
[34]
G. S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu, AID: A benchmark data set for performance evaluation of aerial scene classification, IEEE Trans. Geosci. Remote Sens., vol. 55, no. 7, pp. 3965–3981, 2017.
[35]
Y. Yang and S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification, in Proc. 18th SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, San Jose, CA, USA, 2010, pp. 270–279.
[36]
J. Liu, J. Tang, and G. Wu, Residual feature distillation network for lightweight image super-resolution, in Proc. European Conf. Computer Vision, Glasgow, UK, 2020, pp. 41–55.