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Super-resolution reconstruction (SRR) is a process aimed at enhancing spatial resolution of images, either from a single observation, based on the learned relation between low and high resolution, or from multiple images presenting the same scene.
1903
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S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. on Image Process. , vol. 13, no. 10, pp. 1327–1344, 2004
2004
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R. Hardie, “A fast image super-resolution algorithm using an adaptive wiener filter,” IEEE Trans. on Image Process. , vol. 16, no. 12, pp. 2953–2964, 2007
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H. Demirel and G. Anbarjafari, “Discrete wavelet transform-based satellite image resolution enhancement,” IEEE Trans. on Geoscience and Remote Sensing , vol. 49, no. 6, pp. 1997–2004, 2011
2011
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H. Chavez-Roman and V. Ponomaryov, “Super resolution image generation using wavelet domain interpolation with edge extraction via a sparse representation,” IEEE Geoscience and Remote Sensing Letters , vol. 11, no. 10, pp. 1777–1781, Oct 2014
2014
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C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in Proc. ECCV . Springer, 2014, pp. 184–199
2014
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C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in Proc. ECCV . Springer, 2016, pp. 391–407
2016
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D. Liu, Z. Wang, B. Wen, J. Yang, W. Han, and T. S. Huang, “Robust single image super-resolution via deep networks with sparse prior,” IEEE Trans. on Image Process. , vol. 25, no. 7, pp. 3194–3207, 2016
2016
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W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks,” ArXiv e-prints , Oct. 2017
2017
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C. Ledig, L. Theis, F. Huszár, J. Caballero et al. , “Photo-realistic single image super-resolution using a generative adversarial network.” in Proc. CVPR , vol. 2, no. 3, 2017, p. 4
2017
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P. Benecki, M. Kawulok, D. Kostrzewa, and L. Skonieczny, “Evaluating super-resolution reconstruction of satellite images,” Acta Astronautica , vol. 153, pp. 15–25, 2018
2018
Later among the works it cites.
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A. Ducournau and R. Fablet, “Deep learning for ocean remote sensing: An application of convolutional neural networks for super-resolution on satellite-derived SST data,” in Proc. WPRRS , 2016, pp. 1–6
2016
Cited alongside, same era.
J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proc. IEEE CVPR , 2016, pp. 1646–1654
2016
Cited alongside, same era.
L. Liebel and M. Körner, “Single-image super resolution for multispectral remote sensing data using convolutional neural networks,” in Proc. ISPRSC , 2016, pp. 883–890
2016
Cited alongside, same era.
M. Kawulok, P. Benecki, D. Kostrzewa, and L. Skonieczny, “Evolving imaging model for super-resolution reconstruction,” in Proc GECCO . New York, NY, USA: ACM, 2018, pp. 284–285
2018
Later among the works it cites.
——, “Towards evolutionary super-resolution,” in Applications of Evolutionary Computation , K. Sim and P. Kaufmann, Eds. Cham: Springer International Publishing, 2018, pp. 480–496
2018
Later among the works it cites.