The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Deep Back-Projection Networks for Super-Resolution
Open accessAbstract
The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images. We propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and downsampling layers, providing an error feedback mechanism for projection errors at each stage. We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components. We show that extending this idea to allow concatenation of features across up- and downsampling stages (Dense DBPN) allows us to reconstruct further improve super-resolution, yielding superior results and in particular establishing new state of the art results for large scaling factors such as 8× across multiple data sets.
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Image quality assessment: from error visibility to structural similarity
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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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
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Image Super-Resolution Using Deep Convolutional Networks
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Accurate Image Super-Resolution Using Very Deep Convolutional Networks
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Cited by
Second-Order Attention Network for Single Image Super-Resolution
Dai, T., Cai, J., Zhang, Y., Xia, S.-T., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01132 · Cited by 2,010
Detection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336
Blind Super-Resolution With Iterative Kernel Correction
Gu, J., Lu, H., Zuo, W., & Dong, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00170 · Cited by 580
Unsupervised Degradation Representation Learning for Blind Super-Resolution
Wang, L., Wang, Y., Dong, X., Xu, Q., Yang, J., An, W., & Guo, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01044 · Cited by 412
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
Timofte, R., Gu, S., Wu, J., Van Gool, L., Zhang, L., Yang, M.-H., Haris, M., Shakhnarovich, G., Ukita, N., Hu, S., et al. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00130 · Cited by 341
Self-citation
Metadata
- Authors
- Haris, M., Shakhnarovich, G., & Ukita, N.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1664-1673
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963729050
- Cited by
- 1,599
- References
- 13
- FWCI
- 61.85
- Type
- conference paper
- Language
- en
- Open access
- green
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