Abstract
Owing to the ill-posed nature of the image super-resolution (SR) problem, learning-based approaches typically employ regularization terms in the representation. Current local-patch based face SR approaches weight representation coefficients to obtain adaptive and accurate priors. However, they ignore the fact that heteroskedasticity generally exists both in the observed data and representation coefficients. In this paper, we present a novel adaptive and weighted representation framework for face SR to further exploit adaptive and accurate prior information for different content inputs. Moreover, we enrich patch priors by sampling context patches, and learn the residual high-frequency components for better reconstruction performance. Experiments on the CAS-PEAL-R1 face database show that our proposed approach outperforms state-of-the-arts that include other deep learning based methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, N., Tao, D., Gao, X., Li, X., Li, J.: A comprehensive survey to face hallucination. Int. J. Comput. Vis. 106, 9–30 (2014)
Baker, S., Kanade, T.: Hallucinating faces. In: 2000 IEEE International Conference on Automatic Face and Gesture Recognition, pp. 83–88, March 2000
Wang, X., Tang, X.: Hallucinating face by eigentransformation. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 35, 425–434 (2005)
Lu, T., Hu, R., Han, Z., Jiang, J., Zhang, Y.: From local representation to global face hallucination: a novel super-resolution method by nonnegative feature transformation. In: 2013 Visual Communications and Image Processing (VCIP), pp. 1–6, November 2013
Zhou, H., Hu, J., Lam, K.M.: Global face reconstruction for face hallucination using orthogonal canonical correlation analysis. In: 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 537–542, December 2015
Zhuang, Y., Zhang, J., Wu, F.: Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation. Pattern Recogn. 40(11), 3178–3194 (2007)
Lan, C., Hu, R., Han, Z., Wang, Z.: A face super-resolution approach using shape semantic mode regularization. In: 2010 IEEE International Conference on Image Processing, pp. 2021–2024, September 2010
Chang, H., Yeung, D.-Y., Xiong, Y.: Super-resolution through neighbor embedding. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 1(3), I (2004)
Ma, X., Zhang, J., Qi, C.: Hallucinating face by position-patch. Pattern Recogn. 43(6), 2224–2236 (2010)
Jung, C., Jiao, L., Liu, B., Gong, M.: Position-patch based face hallucination using convex optimization. IEEE Sig. Process. Lett. 18, 367–370 (2011)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8, June 2008
Wang, Z., Hu, R., Wang, S., Jiang, J.: Face hallucination via weighted adaptive sparse regularization. IEEE Trans. Circ. Syst. Video Technol. 24(5), 802–813 (2014)
Jung, C., Jiao, L., Liu, B., Gong, M.: Position-patch based face hallucination using convex optimization. IEEE Sig. Process. Lett. 18(6), 367–370 (2011)
Gao, G., Jing, X.Y., Huang, P., Zhou, Q., Wu, S., Yue, D.: Locality-constrained double low-rank representation for effective face hallucination. IEEE Access 4, 8775–8786 (2016)
Wang, Z., Hu, R., Jiang, J., Han, Z., Shao, Z.: Heteroskedasticity tuned mixed-norm sparse regularization for face hallucination. Multimedia Tools Appl. 75(24), 17273–17301 (2016)
Jiang, J., Hu, R., Wang, Z., Han, Z.: Noise robust face hallucination via locality-constrained representation. IEEE Trans. Multimedia 16(5), 1268–1281 (2014)
Zhang, Y., Zhang, Z., Hu, G., Hancock, E.R.: Face image super-resolution via weighted patches regression. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3881–3886 (2016)
Jiang, J., Chen, C., Huang, K., Cai, Z., Hu, R.: Noise robust position-patch based face super-resolution via Tikhonov regularized neighbor representation. Inf. Sci. 367, 354–372 (2016)
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295–307 (2016)
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR Oral), June 2016
Timofte, R., Gool, L.V.: Adaptive and weighted collaborative representations for image classification. Pattern Recogn. Lett. 43, 127–135 (2014)
Romano, Y., Elad, M.: Con-patch: when a patch meets its context. IEEE Trans. Image Process. 25, 3967–3978 (2016)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: Proceedings, vol. 2011, no. 5, pp. 471–478 (2011)
Tikhonov, A.N., Arsenin, V.Y.: Solution of ill-posed problems. Math. Comput. 32(144), 491 (1978)
Timofte, R., Rothe, R., Gool, L.V.: Seven ways to improve example-based single image super resolution. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1865–1873, June 2016
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 38, 149–161 (2008)
Jiang, J., Hu, R., Wang, Z., Han, Z., Ma, J.: Facial image hallucination through coupled-layer neighbor embedding. IEEE Trans. Circ. Syst. Video Technol. 26, 1674–1684 (2016)
Acknowledgments
This work is supported by the grant of China Scholarship Council, the National Natural Science Foundation of China (61502354,61501413), the Natural Science Foundation of Hubei Province of China (2012FFA099, 2012FFA134, 2013CF125, 2014CFA130, 2015CFB451), Scientific Research Foundation of Wuhan Institute of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Lu, T., Pan, L., Wang, J., Zhang, Y., Wang, Z., Xiong, Z. (2018). AWCR: Adaptive and Weighted Collaborative Representations for Face Super-Resolution with Context Residual-Learning. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-77380-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77379-7
Online ISBN: 978-3-319-77380-3
eBook Packages: Computer ScienceComputer Science (R0)