Fetching the paper…
Reading the bibliography…
Signal degradation is ubiquitous and computational restoration of degraded signal has been investigated for many years.
K. Fukunaga, Introduction to Statistical Patten Recognition (2nd Edition) . San Diego, CA, USA: Academic Press, 1990, ch. 3.1, pp. 51–65
1990
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
I. Csiszár and P. C. Shields, “Information theory and statistics: A tutorial,” Foundations and Trends® in Communications and Information Theory , vol. 1, no. 4, pp. 417–528, 2004
2004
Earlier work this paper cites.
A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Transactions on Image Processing , vol. 21, no. 12, pp. 4695–4708, 2012
2012
Earlier work this paper cites.
M. A. Saad, A. C. Bovik, and C. Charrier, “Blind image quality assessment: A natural scene statistics approach in the DCT domain,” IEEE Transactions on Image Processing , vol. 21, no. 8, pp. 3339–3352, 2012
2012
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, Elements of Information Theory . John Wiley & Sons, 2012
2012
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution,” in ECCV , 2014, pp. 184–199
2014
Earlier work this paper cites.
T. Van Erven and P. Harremos, “Rényi divergence and Kullback-Leibler divergence,” IEEE Transactions on Information Theory , vol. 60, no. 7, pp. 3797–3820, 2014
2014
Earlier work this paper cites.
C. Dong, Y. Deng, C. C. Loy, and X. Tang, “Compression artifacts reduction by a deep convolutional network,” in ICCV , 2015, pp. 576–584
2015
Cited alongside, same era.
Q. Lu, W. Zhou, L. Fang, and H. Li, “Robust blur kernel estimation for license plate images from fast moving vehicles,” IEEE Transactions on Image Processing , vol. 25, no. 5, pp. 2311–2323, 2016
2016
Cited alongside, same era.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in ECCV , 2016, pp. 694–711
2016
Cited alongside, same era.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3142–3155, 2017
2017
Cited alongside, same era.
S. Su, M. Delbracio, J. Wang, G. Sapiro, W. Heidrich, and O. Wang, “Deep video deblurring for hand-held cameras,” in CVPR , 2017, pp. 1279–1288
D. Liu, D. Wang, and H. Li, “Recognizable or not: Towards image semantic quality assessment for compression,” Sensing and Imaging , vol. 18, no. 1, pp. 1–20, 2017
2017
Later among the works it cites.
J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Generative image inpainting with contextual attention,” in CVPR , 2018, pp. 5505–5514
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Blau and T. Michaeli, “The perception-distortion tradeoff,” in CVPR , 2018, pp. 6228–6237
2018
Later among the works it cites.
Y. Blau, R. Mechrez, R. Timofte, T. Michaeli, and L. Zelnik-Manor, “The 2018 PIRM challenge on perceptual image super-resolution,” in ECCV , 2018, pp. 1–22
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, and F. Durand, “Deep bilateral learning for real-time image enhancement,” ACM Transactions on Graphics , vol. 36, no. 4, p. 118, 2017
2017
Cited alongside, same era.
H. Kuang, X. Zhang, Y.-J. Li, L. L. H. Chan, and H. Yan, “Nighttime vehicle detection based on bio-inspired image enhancement and weighted score-level feature fusion,” IEEE Transactions on Intelligent Transportation Systems , vol. 18, no. 4, pp. 927–936, 2017
2017
Cited alongside, same era.
2018
Later among the works it cites.
T. Vu, C. Van Nguyen, T. X. Pham, T. M. Luu, and C. D. Yoo, “Fast and efficient image quality enhancement via desubpixel convolutional neural networks,” in ECCV , 2018, pp. 1–17
2018
Later among the works it cites.