Abstract
Image super-resolution (SR) increases the resolution of the target image, and has become a fundamental image-editing operation for real-world applications. Traditional methods often cause jaggies and blurring artifacts because natural images generally contain a lot of discrete continuities and edges. This paper proposes a new synthesis-based method for image super-resolution at a pixel level that takes advantages of convolution-based edge anti-aliasing. The target images are divided into two components representing, respectively, the high- and low-frequency contents of the images. We perform bicubic interpolation to reconstruct the missing information in the low-frequency component. A patch-based texture synthesis is subsequently adopted to synthesize the high-frequency patches with the final upscaled images. In particular, we also use the efficient edge-based anti-aliasing for correcting the quantization error, restore the high-frequency details damaged by nonlinear example-based synthesis. Our proposed approach generates super-resolution images dynamically and can be fully implemented in GPU parallelization. Experiments confirm the visual superiority of our proposed approach in comparison with competing state-of-the-art techniques.
Similar content being viewed by others
References
Capel D, Zisserman A (2000) Super-resolution enhancement of text image sequences. In: Proceedings of 15th International Conference on Pattern Recognition, 2000, vol 1. IEEE, pp 600–605
Damkat C (2011) Single image super-resolution using self-examples and texture synthesis. SIViP 5(3):343–352
Datsenko D, Elad M (2007) Example-based single document image super-resolution: a global map approach with outlier rejection. Multidim Syst Sign Process 18(2–3):103–121
Elad M, Datsenko D (2009) Example-based regularization deployed to super-resolution reconstruction of a single image. Comput J 52(1):15–30
Fattal R (2007) Image up sampling via imposed edge statistics. ACM Trans Graph (TOG) 26(3):95
Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. JOSA A 4(12):2379–2394
Freedman G, Fattal R (2011) Image and video upscaling from local self-examples. ACM Trans Graph (TOG) 30(2):12
Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56–65
Glasner D., Bagon S., Irani M. (2009) Super-resolution from a single image. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 349–356
Guo K, Yang X, Zha H, Lin W, Yu S (2012) Multiscale semilocal interpolation with antialiasing. IEEE Trans Image Process 21(2):615–625
Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP: Graph Model Image Process 53(3):231–239
Jagadeesh P, Pragatheeswaran J (2011) Image resolution enhancement based on edge directed interpolation using dual treełcomplex wavelet transform. In: 2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, pp 759–763
Jiang W, Lam KM, Shen TZ (2009) Efficient edge detection using simplified gabor wavelets. IEEE Trans Syst Man Cybern B Cybern 39(4):1036–1047
Jiji C, Joshi M, Chaudhuri S (2004) Single-frame image super-resolution using learned wavelet coefficients. Int J Imaging Syst Technol 14(3):105–112
Joshi MV, Chaudhuri S, Panuganti R (2005) A learning-based method for image super-resolution from zoomed observations. IEEE Trans Syst Man Cybern B Cybern 35(3):527–537
Keys R. (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160
Kim KI, Kwon Y (2008) Example-based learning for single-image super-resolution. In: Pattern recognition. Springer, pp 456–465
Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 32(6):1127–1133
Lefebvre S., Hoppe H. (2005) Parallel controllable texture synthesis. ACM Trans Graph (TOG) 24(3):777–786
Lefebvre S, Hoppe H (2006) Appearance-space texture synthesis. In: ACM SIGGRAPH 2006 Papers. ACM, p 548
Li X., Gu Y., Hu S., Martin R (2013). Mixed-domain edge-aware image manipulation
Li X, Lam KM, Qiu G, Shen L, Wang S (2008) An efficient example-based approach for image super-resolution. IEEE, pp 575–580
Li X, Lam KM, Qiu G, Shen L, Wang S (2009) Example-based image super-resolution with class-specific predictors. J Vis Commun Image Represent 20(5):312–322
Liu S, Brown MS, Kim SJ, Tai Y (2010) Colorization for single image super resolution. In: Computer Vision - ECCV 2010 - 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part VI, pp 323–336
Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842
Ng MK, Koo J, Bose N (2002) Constrained total least-squares computations for high-resolution image reconstruction with multisensors. Int J Imaging Syst Technol 12(1):35–42
Nguyen N, Milanfar P, Golub G (2001) A computationally efficient superresolution image reconstruction algorithm. IEEE Trans Image Process 10 (4):573–583
Paris S, Hasinoff SW, Kautz J (2011) Local laplacian filters: edge-aware image processing with a laplacian pyramid. ACM Trans Graph 30(4):68
Piao Y, Shin IH, Park HW (2007) Image resolution enhancement using inter-subband correlation in wavelet domain. In: IEEE International Conference on Image Processing, 2007. ICIP 2007, vol 1. IEEE, pp I–445
Rajan D, Chaudhuri S (2003) Simultaneous estimation of super-resolved scene and depth map from low resolution defocused observations. IEEE Trans Pattern Anal Mach Intell 25(9):1102–1117
Shan Q, Li Z, Jia J, Tang CK (2008) Fast image/video upsampling. In: ACM Transactions on Graphics (TOG), vol 27. ACM, p 153
Shechtman E., Irani M. (2007) Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE, pp 1–8
Sheng B, Sun H, Wu Y, Thalmann D (2013) Parallel iso/aniso-scale surface texturing guided in gabor space. In: SIGGRAPH Asia 2013 technical briefs. ACM, p 7
Suetake N., Sakano M., Uchino E. (2008) Image super-resolution based on local self-similarity. Opt Rev 15(1):26–30
Sun J., Xu Z., Shum H.Y. (2011) Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans Image Process 20(6):1529–1542
Tang Y, Yan P, Yuan Y, Li X (2011) Single-image super-resolution via local learning. Int J Mach Learn Cybern 2(1):15–23
Tekalp AM, Ozkan MK, Sezan MI (1992) High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992. ICASSP-92, vol 3. IEEE, pp 169–172
Tong X, Zhang J, Liu L, Wang X, Guo B, Shum H (2002) Synthesis of bidirectional texture functions on arbitrary surfaces. ACM Trans Graph 21(3):665–672
Tsai RY, Huang TS (1984) Multiframe image restoration and registration. In: Advances in Computer Vision and Image Processing, vol 1. JAI Press, Greenwich, pp 317–339
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
Wang L, Wu H, Pan C (2014) Fast image upsampling via the displacement field. IEEE Trans Image Process 23(12):5123–5135
Yang L, Sander PV, Lawrence J, Hoppe H (2011) Antialiasing recovery. ACM Trans Graph (TOG) 30(3):22
Zhang K, Gao X, Tao D, Li X (2012) Single image super-resolution with non-local means and steering kernel regression. IEEE Trans Image Process 21 (11):4544–4556
Acknowledgments
The authors would like to thank all reviewers for their helpful suggestions and constructive comments. The work is supported by the National Natural Science Foundation of China (No.61202154, 61572316, 61272326, 61133009), the National Basic Research Project of China (No. 2011CB302203), National High-tech R&D Program of China (863 Program)(Grant No. 2015AA011604), and Shanghai Pujiang Program (No.13PJ1404500), the Science and Technology Commission of Shanghai Municipality Program (No. 13511505000), the Open Projects Program of National Laboratory of Pattern Recognition, and the Open Project Program of the State Key Lab of CAD&CG (Grant No. A1401), Zhejiang University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jiang, X., Sheng, B., Lin, W. et al. Antialiased super-resolution with parallel high-frequency synthesis. Multimed Tools Appl 76, 543–560 (2017). https://doi.org/10.1007/s11042-015-3049-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-3049-8