Abstract
Example learning-based super-resolution (SR) methods are effective to generate a high-resolution (HR) image from a single low-resolution (LR) input. And these SR methods have shown a great potential for many practical applications. Unfortunately, most of popular example learning-based approaches extract features from limited training images. These training images are insufficient for super resolution task. Our work is to transfer some supplemental information from other domains. Therefore, in this paper, a new algorithm Transfer Learning based on A+ (TLA) is proposed for image super-resolution task. First, we transfer supplemental information from other datasets to construct a new dictionary. Then, in sample selection, more training samples are supplemented to the basic training samples. In experiments, we seek to explore what types of images can provide more appropriate information for super-resolution task. Experimental results indicate that our approach is superior to A+ when transferring images containing similar content with original data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Deng, C., Xu, J., Zhang, K., Tao, D., Gao, X., Li, X.: Similarity constraints-based structured output regression machine: an approach to image super-resolution. IEEE Trans. Neural Netw. Learn. Syst. (2015). doi:10.1109/TNNLS.2015.2468069
Yu, J., Gao, X., Tao, D., Li, X., Zhang, K.: A unified learning framework for single image super-resolution. IEEE Trans. Neural Netw. Learn. Syst. 25(4), 780–792 (2014)
Zhu, Y., Li, K., Jiang, J.: Video super-resolution based on automatic key-frame selection and feature-guided variational optical flow. Sig. Process. Image Commun. 29(8), 875–886 (2014)
Zhang, K., Zhou, X., Zhang, H., Zuo, W.: Revisiting single image super-resolution under internet environment: blur kernels and reconstruction algorithms. In: Ho, Y.-S., Sang, J., Ro, Y.M., Kim, J., Wu, F. (eds.) PCM 2015. LNCS, vol. 9314, pp. 677–687. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24075-6_65
Li, K., Zhu, Y., Yang, J., Jiang, J.: Video super-resolution using an adaptive super-pixel guided auto-regressive model. Pattern Recogn. 51, 59–71 (2016)
Li, J., Qiu, M.K., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72(5), 666–677 (2012)
Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16817-3_8
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: CVPR, pp. 1–8. IEEE (2008)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012). doi:10.1007/978-3-642-27413-8_47
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Zhang, D., Si, L.: Multiple instance transfer learning. In: IEEE International Conference on Data Mining Workshops, pp. 406–411. IEEE (2009)
Dai, D., Kroeger, T., Timofte, R., Van Gool, L.: Metric imitation by manifold transfer for efficient vision applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3527–3536 (2015)
Dong, C., Deng, Y., Change Loy, C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 576–584 (2015)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: IEEE International Conference on Computer Vision, pp. 416–423. IEEE (2001)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Zhang, Y., Zhang, Y., Zhang, J., Wang, H., Dai, Q.: Single image super-resolution via iterative collaborative representation. In: Ho, Y.-S., Sang, J., Ro, Y.M., Kim, J., Wu, F. (eds.) PCM 2015. LNCS, vol. 9315, pp. 63–73. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24078-7_7
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: CVPR, pp. 3606–3613 (2014)
Zhong, S., Liu, Y., Chen, Q.: Visual orientation inhomogeneity based scale invariant feature transform. Expert Syst. Appl. 42(13), 5658–5667 (2015)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61502311), the Natural Science Foundation of Guangdong Province (No. 2016A030310053), the Science and Technology Innovation Commission of Shenzhen under Grant (No. JCYJ20150324141711640), the Strategic Emerging Industry Development Foundation of Shenzhen (No. JCY20130326105637578), the Shenzhen University research funding (201535), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), and the Tencent Rhinoceros Birds Scientific Research Foundation (2015).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Su, M., Zhong, Sh., Jiang, Jm. (2016). Transfer Learning Based on A+ for Image Super-Resolution. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-47650-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47649-0
Online ISBN: 978-3-319-47650-6
eBook Packages: Computer ScienceComputer Science (R0)