The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The 2018 PIRM Challenge on Perceptual Image Super-Resolution
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Citations
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253 · cited 1×
“Note that we also tested the full-reference measures VIF [32], FSIM [49] and MS-SSIM [46], and the no-reference measures CORNIA [48] and BLIINDS [30], which all failed to correctly assess the perceptual quality.”
MethodNo-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064
Cited by
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00475 · Cited by 760
Self-citationDiff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
Yi, X., Xu, H., Zhang, H., Tang, L., & Ma, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01130 · Cited by 305
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
Liu, M.-Y., Huang, X., Yu, J., Wang, T.-C., & Mallya, A. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3049196 · Cited by 236
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Blau, Y., & Michaeli, T. (2019) · arXiv · doi:10.48550/arxiv.1901.07821 · Cited by 198
Self-citation
Metadata
- Authors
- Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., & Zelnik-Manor, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 334-355
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2891165135
- Cited by
- 593
- References
- 7
- FWCI
- 28.76
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: The 2018 PIRM Challenge on Perceptual Image Super-Resolution. https://app.cochat.ai/papers/10.1007/978-3-030-11021-5_21
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- Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., & Zelnik-Manor, L. (2019). The 2018 PIRM Challenge on Perceptual Image Super-Resolution. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-11021-5_21
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
