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Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression
Open accessAbstract
Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.
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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
Nonlinear total variation based noise removal algorithms
Rudin, L. I., Osher, S., & Fatemi, E. (1992) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 15,704
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“Examples are edge-directed interpolation [3]–[5], normalized convolution [28], bilateral filter [29], [30], and steering kernel regression (SKR) [31].”
BackgroundA 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
Image Super-Resolution Via Sparse Representation
Yang, J., Wright, J., Huang, T. S., & Ma, Y. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2050625 · Cited by 5,217
On Single Image Scale-Up Using Sparse-Representations
Zeyde, R., Elad, M., & Protter, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-27413-8_47 · Cited by 3,352
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
Fast and Robust Multiframe Super Resolution
Farsiu, S., Robinson, M. D., Elad, M., & Milanfar, P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.834669 · Cited by 2,090
New edge-directed interpolation
Li, X., & Orchard, M. T. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.951537 · Cited by 2,023
Self-citationSuper-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
Non-local sparse models for image restoration
Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459452 · Cited by 1,666
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
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
Tao, D., Li, X., Wu, X., & Maybank, S. J. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1096 · Cited by 1,140
Self-citationDong, W., Zhang, L., Shi, G., & Wu, X. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2108306 · Cited by 1,124
Dong, W., Lei Zhang, A., Shi, G., Member, S., Xiaolin Wu, C., & Member, S. (2011) · Cited by 1,023
An edge-guided image interpolation algorithm via directional filtering and data fusion
Zhang, L., & Wu, X. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.877407 · Cited by 1,020
Multi-frame image restoration and registration
Huang, T. S. (1984) · Medical Entomology and Zoology · Cited by 976
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
Kim, K. I., & Kwon, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.25 · Cited by 956
Image super-resolution using gradient profile prior
Sun, J., Xu, Z., & Shum, H.-Y. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587659 · Cited by 916
Multiframe image restoration and registration
Tsai, R. (1984) · Medical Entomology and Zoology · Cited by 870
On the origin of the bilateral filter and ways to improve it
Elad, M. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.801126 · Cited by 791
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
Protter, M., Elad, M., Takeda, H., & Milanfar, P. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2008067 · Cited by 707
Geometric Mean for Subspace Selection
Tao, D., Li, X., Wu, X., & Maybank, S. J. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.70 · Cited by 583
Self-citationRecursive reconstruction of high resolution image from noisy undersampled multiframes
Kim, S. P., Bose, N. K., & Valenzuela, H. M. (1990) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.56062 · Cited by 520
NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization
Guan, N., Tao, D., Luo, Z., & Yuan, B. (2012) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2012.2190406 · Cited by 468
Self-citationSuper resolution using edge prior and single image detail synthesis
Tai, Y.-W., Liu, S., Brown, M. S., & Lin, S. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539933 · Cited by 354
Image Super-Resolution With Sparse Neighbor Embedding
Gao, X., Zhang, K., Tao, D., & Li, X. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2190080 · Cited by 313
Self-citationOnline Nonnegative Matrix Factorization With Robust Stochastic Approximation
Guan, N., Tao, D., Luo, Z., & Yuan, B. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2197827 · Cited by 287
Self-citationA multi-frame image super-resolution method
Li, X., Hu, Y., Gao, X., et al. (2009) · Signal Processing · doi:10.1016/j.sigpro.2009.05.028 · Cited by 281
Self-citationImage Quality Assessment Based on Multiscale Geometric Analysis
Gao, X., Lu, W., Tao, D., & Li, X. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2018014 · Cited by 252
Self-citationNormalized and differential convolution
Knutsson, H., & Westin, C.-F. (2002) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341081 · Cited by 242
Ensemble Manifold Regularization
Geng, B., Tao, D., Xu, C., Yang, L., & Hua, X.-S. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.57 · Cited by 227
Self-citationManifold elastic net: a unified framework for sparse dimension reduction
Zhou, T., Tao, D., & Wu, X. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0182-x · Cited by 195
Self-citationA nonlocal-means approach to exemplar-based inpainting
Wong, A., & Orchard, J. (2008) · 2008 15th IEEE International Conference on Image Processing · doi:10.1109/icip.2008.4712326 · Cited by 184
Non-Negative Patch Alignment Framework
Guan, N., Tao, D., Luo, Z., & Yuan, B. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2157359 · Cited by 181
Self-citationJoint Learning for Single-Image Super-Resolution via a Coupled Constraint
Gao, X., Zhang, K., Tao, D., & Li, X. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2161482 · Cited by 163
Self-citationSparse transfer learning for interactive video search reranking
Tian, X., Tao, D., & Rui, Y. (2012) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2240136.2240139 · Cited by 120
Self-citationNon-Local Kernel Regression for Image and Video Restoration
Zhang, H., Yang, J., Zhang, Y., & Huang, T. S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15558-1_41 · Cited by 118
Single-image super-resolution via local learning
Tang, Y., Yan, P., Yuan, Y., & Li, X. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0011-6 · Cited by 114
Self-citationColor to Gray: Visual Cue Preservation
Song, M., Tao, D., Chen, C., et al. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.74 · Cited by 105
Self-citationPartially Supervised Neighbor Embedding for Example-Based Image Super-Resolution
Zhang, K., Gao, X., Li, X., et al. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2048606 · Cited by 104
Self-citationZernike-Moment-Based Image Super Resolution
Gao, X., Wang, Q., Li, X., et al. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2134859 · Cited by 85
Self-citationSingle image super resolution with high resolution dictionary
Mu, G., Gao, X., Zhang, K., Li, X., & Tao, D. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6115630 · Cited by 18
Self-citationSingle-Image Super-Resolution via Sparse Coding Regression
Tang, Y., Yuan, Y., Yan, P., & Li, X. (2011) · International Conference on Image and Graphics · doi:10.1109/icig.2011.63 · Cited by 17
Self-citationSingle-image super-resolution based on semi-supervised learning
Tang, Y., Yuan, Y., Yan, P., et al. (2011) · doi:10.1109/acpr.2011.6166563 · Cited by 16
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Deep Networks for Image Super-Resolution with Sparse Prior
Wang, Z., Liu, D., Yang, J., Han, W., & Huang, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.50 · Cited by 784
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
Liu, D., Wang, Z., Wen, B., Yang, J., Han, W., & Huang, T. S. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2564643 · Cited by 266
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
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
Metadata
- Authors
- Zhang, K., Gao, X., Tao, D., & Li, X.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2012-11
- Volume
- 21
- Issue
- 11
- Pages
- 4544-4556
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1992408872
- Cited by
- 576
- References
- 47
- FWCI
- 28.85
- Type
- journal article
- Language
- en
- Open access
- green
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