The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Image super-resolution using gradient profile prior
Abstract
In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.
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Citations
Cites
A Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
Example-based super-resolution
Freeman, W. T., Jones, T. R., & Pasztor, E. C. (2002) · IEEE Computer Graphics and Applications · doi:10.1109/38.988747 · Cited by 2,498
New edge-directed interpolation
Li, X., & Orchard, M. T. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.951537 · Cited by 2,023
Super-resolution through neighbor embedding
Chang, H., Yeung, D.-Y., & Xiong, Y. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315043 · Cited by 1,902
Removing camera shake from a single photograph
Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., & Freeman, W. T. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141956 · Cited by 1,815
Freeman, W. T., Pasztor, E. C., & Carmichael, O. T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026501619075 · Cited by 1,459
Kernel Regression for Image Processing and Reconstruction
Takeda, H., Farsiu, S., & Milanfar, P. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.888330 · Cited by 1,340
Cubic splines for image interpolation and digital filtering
Hou, H., & Andrews, H. (1978) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1978.1163154 · Cited by 1,270
Limits on super-resolution and how to break them
Baker, S., & Kanade, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1033210 · Cited by 1,205
Limits on super-resolution and how to break them
Baker, S., & Kanade, T. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.854852 · Cited by 1,184
Cited by
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
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
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
“Early methods include interpolation such as bicubic interpolation and Lanczos resampling [7] more powerful methods utilizing statistical image priors [20, 13] or internal patch recurrence [9].”
MethodSuper-resolution from a single image
Glasner, D., Bagon, S., & Irani, M. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459271 · Cited by 1,889
Image super-resolution: the techniques, applications, and future
Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., & Zhang, L. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.05.002 · Cited by 554
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution
Huang, H., He, R., Sun, Z., & Tan, T. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.187 · Cited by 518
Accurate Blur Models vs. Image Priors in Single Image Super-resolution
Efrat, N., Glasner, D., Apartsin, A., Nadler, B., & Levin, A. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.352 · Cited by 200
Diffusion Models, Image Super-Resolution, and Everything: A Survey
Moser, B. B., Shanbhag, A. S., Raue, F., Frolov, S., Palacio, S., & Dengel, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476671 · Cited by 117
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
Wang, Y., Wang, L., Wang, H., & Li, P. (2019) · IEEE Access · doi:10.1109/access.2019.2903582 · Cited by 143
No-reference perceptual image quality metric using gradient profiles for JPEG2000
Liang, L., Wang, S., Chen, J., Ma, S., Zhao, D., & Gao, W. (2010) · Signal Processing: Image Communication · doi:10.1016/j.image.2010.01.007 · Cited by 74
MRI Super-Resolution With Partial Diffusion Models
Zhao, K., Pang, K., Hung, A. L. Y., Zheng, H., Yan, R., & Sung, K. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3483109 · Cited by 36
Image super-resolution with densely connected convolutional networks
Kuang, P., Ma, T., Chen, Z., & Li, F. (2019) · Applied Intelligence · doi:10.1007/s10489-018-1234-y · Cited by 33
Zhang, K., Zhou, X., Zhang, H., & Zuo, W. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24075-6_65 · Cited by 24
Metadata
- Authors
- Sun, J., Xu, Z., & Shum, H.-Y.
- Venue
- 2008 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2008-06
- Pages
- 1-8
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2163935418
- Cited by
- 916
- References
- 35
- FWCI
- 17.63
- Type
- conference paper
- Language
- en
- Open access
- closed
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