A Study on Lightweight CNN-based Interpolation Method for Satellite Images |
Kim, Hyun-ho
(National Satellite Operation & Application Center, Korea Aerospace Research Institute)
Seo, Doochun (National Satellite Operation & Application Center, Korea Aerospace Research Institute) Jung, JaeHeon (National Satellite Operation & Application Center, Korea Aerospace Research Institute) Kim, Yongwoo (Department of System Semiconductor Engineering, Sangmyung University) |
1 | Hu, J., L. Shen, and G. Sun, 2018. Squeeze-and-excitation networks, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, Jun. 18-23, pp. 7132-7141. https://doi.org/10.1109/CVPR.2018.00745 DOI |
2 | Hui, Z., X. Wang, and X. Gao, 2018. Fast and accurate single image super-resolution via information distillation network, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, Jun. 18-23, pp. 723-731. https://doi.org/10.1109/CVPR.2018.00082 DOI |
3 | Lanaras, C., J. Bioucas-Dias, S. Galliani, E. Baltsavias, and K. Schindler, 2018. Super-resolution of Sentinel2 images: Learning a globally applicable deep neural network, ISPRS Journal of Photogrammetry and Remote Sensing, 146: 305-319. https://doi.org/10.1016/j.isprsjprs.2018.09.018 DOI |
4 | Tuna, C., G. Unal, and E. Sertel, 2018. Single-frame super resolution of remote-sensing images by convolutional neural networks, International Journal of Remote Sensing, 39(8): 2463-2479. https://doi.org/10.1080/01431161.2018.1425561 DOI |
5 | Bosch, M., C.M. Gifford, and P.A. Rodriguez, 2018. Super-resolution for overhead imagery using densenets and adversarial learning, Proc. of 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, Mar. 12-15, pp. 1414-1422. https://doi.org/10.1109/WACV.2018.00159 DOI |
6 | Dong, C., C.C. Loy, K. He, and X. Tang, 2014. Learning a deep convolutional network for image super-resolution, Proc. of the European Conference on Computer Vision, Zurich, Switzerland, Sep. 6-12, vol. 8692, pp. 184-199. https://doi.org/10.1007/978-3-319-10593-2_13 DOI |
7 | Lai, W.S., J.B. Huang, N. Ahuja, and M.H. Yang, 2018. Fast and accurate image super-resolution with deep laplacian pyramid networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11): 2599-2613. https://doi.org/10.1109/TPAMI.2018.2865304 DOI |
8 | Huang, G., Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, 2017. Densely connected convolutional networks, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, Jun. 21-26, pp. 4700-4708. https://doi.org/10.1109/CVPR.2017.243 DOI |
9 | Hui, Z., X. Gao, Y. Yang, and X. Wang, 2019. Lightweight image super-resolution with information multi-distillation network, Proc. of the 27th ACM International Conference on Multimedia, Nice, France, Oct. 21-25, pp. 2024-2032. https://doi.org/10.1145/3343031.3351084 DOI |
10 | Keys, R., 1981. Cubic convolution interpolation for digital image processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(6): 1153-1160. https://doi.org/10.1109/TASSP.1981.1163711 DOI |
11 | Yan, Q., Y. Xu, X. Yang, and T.Q. Nguyen, 2015. Single image super resolution based on gradient profile sharpness, IEEE Transactions on Image Processing, 24(10): 3187-3202. https://doi.org/10.1109/TIP.2015.2414877 DOI |
12 | Ledig, C., L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, and W. Shi, 2017. Photo-realistic single image super-resolution using a generative adversarial network, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, Jun. 21-26, pp. 4681-4690. https://doi.org/10.1109/CVPR.2017.19 DOI |
13 | Liebel, L. and M. Korner, 2016. Single-image super resolution for multispectral remote sensing data using convolutional neural networks, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41(B3): 883-890. https://doi.org/10.5194/isprs-archives-XLI-B3-883-2016 DOI |
14 | Wang, Z., A.C. Bovik, and H.R. Sheikh, 2004. Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 13(4): 600-612. https://doi.org/10.1109/TIP.2003.819861 DOI |