Abstract
In this paper, we propose a super resolution (SR) method for synthetic images using FeatureMatch. Existing state-of-the-art super resolution methods are learning based methods, where a pair of low-resolution and high-resolution dictionary pair are trained, and this trained pair is used to replace patches in low-resolution image with appropriate matching patches from the high-resolution dictionary. In this paper, we show that by using Approximate Nearest Neighbour Fields (ANNF), and a common source image, we can by-pass the learning phase, and use a single image for dictionary. Thus, reducing the dictionary from a collection obtained from hundreds of training images, to a single image. We show that by modifying the latest developments in ANNF computation, to suit super resolution, we can perform much faster and more accurate SR than existing techniques. To establish this claim we will compare our algorithm against various state-of-the-art algorithms, and show that we are able to achieve better and faster reconstruction without any training phase.
Similar content being viewed by others
Notes
http://www.rarewallpapers.com/_wallpapers/apple-mac-icon-collage-1920x1200.jpg
http://www.ifp.illinois.edu/~jyang29/resources.html
http://www.cs.technion.ac.il/~elad/software/
http://www.csee.wvu.edu/~xinl/code/nedi.zip
References
Barnes C, Shechtman E, Finkelstein A, Goldman DB (2009) PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans Graph 28:24:1–24:11
Borman S, Stevenson RL (1998) Super-resolution from image sequences-a review. In: Midwest symposium on circuits and systems, pp 374–378
Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, vol 1, pp I–275–I–282
Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306
Farsiu S, Robinson MD, Elad M, Milanfar P (2004) Fast and robust multiframe super resolution. IEEE Trans Image Process 13(10):1327–1344
Gao X, Wang Q, Li X, Tao D, Zhang K (2011) Zernike-moment-based image super resolution. Trans Image Process 20(10):2738–2747
Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: International conference on computer vision
Hou HS, Andrews HC (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Processing 26(6):508–517
Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Machine Intell 32(6):1127–1133
Kim S, Su WY (1993) Recursive high-resolution reconstruction of blurred multiframe images. IEEE Trans Image Process 2(4):534–539
Li X, Hu Y, Gao X, Tao D, Ning B (2010) A multi-frame image super-resolution method. Signal Process 90(2):405–414
Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527
Protter M, Elad M, Takeda H, Milanfar P (2009) Generalizing the non-local-means to super-resolution reconstruction. In: IEEE transactions on image processing, p 36
Ramakanth SA, Babu RV (2012) FeatureMatch: an efficient low dimensional patchmatch technique. In: Proceedings of the eighth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP ’12, pp 45:1–45:7
Ramakanth SA, Babu RV (2014) Super resolution using a single image dictionary. In: IEEE international conference on electronics, computing and communication technologies
Ramakanth SA, Venkatesh Babu R (2014) FeatureMatch: a general ANNF estimation technique and its applications. IEEE Trans Image Process 23(5):2193-2205
Sudarshan S, Babu RV (2012) Super resolution via sparse representation in l 1 framework. In: ICVGIP, pp 77:1–77:7
Sun J, Xu Z, Shum HY (2008) Image super-resolution using gradient profile prior. In: IEEE conference on computer vision and pattern recognition
Viola PA, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR (1), pp 511–518
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Yang J, Wang Z, Lin Z, Cohen S, Huang T (2012) Coupled dictionary training for image super-resolution. IEEE Trans Image Process 21(8):3467–3478
Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation, vol 19
Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: International conference on curves and surfaces, pp 711–730
Zhang D, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238
Zhang H, Yang J, Zhang Y, Huang TS (2010) Non-local kernel regression for image and video restoration. In: Europian conference on computer vision, vol 6313. Springer, Berlin Heidelberg, pp 566–579
Zhang K, Gao X, Tao D, Li X (2012) Multi-scale dictionary for single image super-resolution. In: Computer Vision and Pattern Recognition, pp 1114–1121
Acknowledgments
This work was supported by Joint Advanced Technology Programme (JATP), Indian Institute of Science, Bangalore, India.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ramakanth, S.A., Babu, R.V. Synthetic image super resolution using FeatureMatch. Multimed Tools Appl 74, 6691–6707 (2015). https://doi.org/10.1007/s11042-014-1925-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-014-1925-2