Abstract
In this paper, we have proposed a novel method for image super-resolution using single image. Based on structural similarity index, an image similar to input low-resolution (LR) image is selected from the database and two separate external dictionaries i.e. smooth and textured, are formed from the selected image based on their variances. Different features are used for representation of different type of patches. For smooth patches norm luminance is used as feature vector and for textured patches it consist of first and second order gradients. In neighbor embedding process, a new parameter in combination with Euclidean distance has been introduced to eliminate outliers. Extensive simulations are performed to show superiority of the method.
Similar content being viewed by others
REFERENCES
K. Nasrollahi and T. B. Moeslund, “Super-resolution: a comprehensive survey,” Mach. Vision Appl. 25 (6), 1423–1468 (2014). https://doi.org/10.1007/s00138-014-0623-4
G. Pandey and U. Ghanekar, “A compendious study of super-resolution techniques by single image,” Optik 166, 147–160 (2018). https://doi.org/10.1016/j.ijleo.2018.03.103
G. Pandey and U. Ghanekar, “Classification of priors and regularization techniques appurtenant to single image super-resolution,” Visual Comput., 1–14 (2019). https://doi.org/10.1007/s00371-019-01729-z
H. Chang, D.-Y. Yeung, and Y. Xiong, “Super-resolution through neighbor embedding,” in Proc. 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004) (Washington, DC, USA, 2004), Vol. 1, pp. I-275 – I-282. https://doi.org/10.1109/cvpr.2004.1315043.
J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19 (11), 2861–2873 (2010). https://doi.org/10.1109/tip.2010.2050625
Y. Li, W. Dong, X. Xie, G. Shi, J. Wu, and X. Li, “Image super-resolution with parametric sparse model learning,” IEEE Trans. Image Process. 27 (9), 4638–4650 (2018). https://doi.org/10.1109/tip.2018.2837865
X. Li, G. Cao, Y. Zhang, and B. Wang, “Single image super-resolution via adaptive sparse representation and low-rank constraint,” J. Visual Commun. Image Represent. 55, 319–330 (2018). https://doi.org/10.1016/j.jvcir.2018.06.012
K. Zhang, X. Gao, D. Tao, and X. Li, “Single image super-resolution with multiscale similarity learning,” IEEE Trans. Neural Networks Learn. Syst. 24 (10), 1648–1659 (2013). https://doi.org/10.1109/tnnls.2013.2262001
K. Zhang, X. Gao, X. Li, and D. Tao, “Partially supervised neighbor embedding for example-based image super-resolution,” IEEE J. Sel. Top. Signal Process. 5 (2), 230–239 (2011). https://doi.org/10.1109/jstsp.2010.2048606
K. Guo, X. Yang, W. Lin, R. Zhang, and S. Yu, “Learning-based super-resolution method with a combining of both global and local constraints,” IET Image Process. 6 (4), 337–344 (2012). https://doi.org/10.1049/iet-ipr.2010.0430
X. Li, H. He, Z. Yin, F. Chen, and J. Cheng, “Single image super-resolution via subspace projection and neighbor embedding,” Neurocomput. 139, 310–320 (2014). https://doi.org/10.1016/j.neucom.2014.02.026
S. Yang, Z. Wang, L. Zhang, and M. Wang, “Dual-geometric neighbor embedding for image super resolution with sparse tensor,” IEEE Trans. Image Process. 23 (7), 2793–2803 (2014). https://doi.org/10.1109/tip.2014.2319742
J. Jiang, X. Ma, C. Chen, T. Lu, Z. Wang, and J. Ma, “Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means,” IEEE Trans. Multimedia 19 (1), 15–26 (2017). https://doi.org/10.1109/tmm.2016.2599145
C. Zhang, W. Liu, J. Liu, C. Liu, and C. Shi, “Sparse representation and adaptive mixed samples regression for single image super-resolution,” Signal Process.: Image Commun. 67, 79–89 (2018). https://doi.org/10.1016/j.image.2018.06.001
T. M. Chan and J. Zhang, “An improved super-resolution with manifold learning and histogram matching,” in Advances in Biometrics, ICB 2006, Ed. by D. Zhang and A. K. Jain, Lecture Notes in Computer Science (Springer, Berlin, Heidelberg, 2005), Vol. 3832, pp. 756–762. https://doi.org/10.1007/11608288_101.
D. Mishra, B. Majhi, P. K. Sa, and R. Dash, “Development of robust neighbor embedding based super-resolution scheme,” Neurocomput. 202, 49–66 (2016). https://doi.org/10.1016/j.neucom.2016.04.013
R. Timofte, V. De, and L. Van Gool, “Anchored neighborhood regression for fast example-based super-resolution,” in Proc. 2013 IEEE Int. Conf. on Computer Vision (ICCV 2013) (Sydnei, Australia, 2013), pp. 1920–1927. https://doi.org/10.1109/iccv.2013.241.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004). https://doi.org/10.1109/tip.2003.819861
Berkeley Segmentation Dataset: Images. Available at: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300/html/dataset/images.html.
Q. Zhu, L. Sun, and C. Cai, “Non-local neighbor embedding for image super- resolution through FoE features,” Neurocomput. 141, 211–222 (2014). https://doi.org/10.1016/j.neucom.2014.03.013
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors declare that they have no conflicts of interest.
Additional information
Garima Pandey completed her B.Tech. in electronics and communication engineering and M.Tech. in communication engineering. Presently doing PhD from National Institute of Technology Kurukshetra, Kurukshtera, Haryana, India. Active in field of Image Processing and Super-resolution.
Umesh Ghanekar completed his M.Tech. degree in Electronics andCommunication Engineering in 1988 from Indian Institute of Technology, Roorkee, India and PhD in computer engineering in 2013 from National Institute of Technology Kurukshetra, Kurukshtera, Haryana, India. Presently he is a Professor in the Department of Electronics and Communication Engineering at N.I.T. Kurukshetra. His research interests include signal and image processing.
Rights and permissions
About this article
Cite this article
Pandey, G., Ghanekar, U. Variance Based External Dictionary for Improved Single Image Super-Resolution. Pattern Recognit. Image Anal. 30, 70–75 (2020). https://doi.org/10.1134/S1054661820010101
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1054661820010101