Abstract
Single image super-resolution is devoted to generating a high-resolution image from a low-resolution one, which has been a research hotspot for its significant applications. A novel method that is totally based on the single input image itself is proposed in this paper. Firstly, a local-feature based interpolation method where both edge pixel property and location information are taken into consideration is presented to obtain a better initialization. Then, a dynamic lightweight database of self-examples is built with the aid of our in-depth study on self-similarity, from which adaptive linear regressions are learned to directly map the low-resolution patch into its high-resolution version. Furthermore, a gradually upscaling strategy accompanied by iterative optimization is employed to enhance the consistency at each step. Even without any external information, extensive experimental comparisons with state-of-the-art methods on standard benchmarks demonstrate the competitive performance of the proposed scheme in both visual effect and objective evaluation.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Fan C, Wang L, Liu P, Lu K, Liu D. Compressed sensing based remote sensing image reconstruction via employing similarities of reference images. Multimedia Tools & Applications, 2016, 75(19): 1-25.
Lu H, Wei J, Wang L, Liu P, Liu Q, Wang Y, Deng X. Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation. Remote Sensing, 2016, 8(6): Article No. 499.
Wang L, Lu K, Liu P. Compressed sensing of a remote sensing image based on the priors of the reference image. IEEE Geoscience & Remote Sensing Letters, 2015, 12(4): 736-740.
Greenspan H. Super-resolution in medical imaging. The Computer Journal, 2009, 52(1): 43-63.
Zhang M, Desrosiers C, Qu Q, Guo F, Zhang C. Medical image super-resolution with non-local embedding sparse representation and improved IBP. In Proc. the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2016, pp.888-892.
Jiang J, Hu R, Wang Z, Han Z. Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning. IEEE Transactions on Image Processing, 2014, 23(10): 4220-4231.
Jiang J, Chen C, Ma J, Wang Z, Wang Z, Hu R. SRLSP: A face image super-resolution algorithm using smooth regression with local structure prior. IEEE Transactions on Multimedia, 2017, 19(1): 27-40.
Wang N, Tao D, Gao X, Li X, Li J. A comprehensive survey to face hallucination. International Journal of Computer Vision, 2014, 106(1): 9-30.
Du S, Ibrahim M, Shehata M, Badawy W. Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on Circuits & Systems for Video Technology, 2013, 23(2): 322-336.
Tian Y, Yap K H, He Y. Vehicle license plate superresolution using soft learning prior. Multimedia Tools & Applications, 2012, 60(3): 519-535.
Cheng M M, Hou Q B, Zhang S H, Rosin P L. Intelligent visual media processing: When graphics meets vision. Journal of Computer Science and Technology, 2017, 32(1): 110-121.
Ma G H, Zhang M L, Li X M, Zhang C M. Image smoothing based on image decomposition and sparse high frequency gradient. Journal of Computer Science and Technology, 2018, 33(3): 502-510.
Keys R. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, 29(6): 1153-1160.
Zhang C, Zhang X, Li X, Cheng F. Cubic surface fitting to image with edges as constraints. In Proc. the 2013 IEEE International Conference on Image Processing, September 2013, pp.1046-1050.
Li X, Orchard M T. New edge-directed interpolation. IEEE Trans Image Process, 2001, 10(10): 1521-1527.
Li X, Zhang C, Yue Y, Wang K. Cubic surface fitting to image by combination. SCIENCE CHINA Information Sciences, 2010, 53(7): 1287-1295.
Duan Q, Wang L, Twizell E H. A new bivariate rational interpolation based on function values. Information Sciences, 2004, 166(1/2/3/4): 181-191.
Zhang Y, Fan Q, Bao F, Liu Y, Zhang C. Single-image super-resolution based on rational fractal interpolation. IEEE International Conference on Image Processing, 2018, 27(8): 3782-3797.
Chang H, Yeung D Y, Xiong Y. Super-resolution through neighbor embedding. In Proc. the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2004, pp.275-282.
Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution. IEEE Computer Graphics & Applications, 2002, 22(2): 56-65.
Yang J, Wright J, Huang T, Ma Y. Image super-resolution as sparse representation of raw image patches. In Proc. the 2008 IEEE Conference on Computer Vision and Pattern Recognition, June 2008, Article No. 308.
Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations. In Proc. the 7th International Conference on Curves and Surfaces, June 2010, pp.711-730.
Freedman G, Fattal R. Image and video upscaling from local self-examples. ACM Transactions on Graphics, 2011, 30(2): Article No. 12.
Yang C Y, Huang J B, Yang M H. Exploiting selfsimilarities for single frame super-resolution. In Proc. the 10th Asian Conference on Computer Vision, November 2010, pp.497-510.
Yang J, Wright J, Huang T S, Ma Y. Image super resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.
Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.
Timofte R, de Smet V, van Gool L. Anchored neighborhood regression for fast example-based super-resolution. In Proc. the 2013 IEEE International Conference on Computer Vision, December 2013, pp.1920-1927.
Timofte R, de Smet V, van Gool L. A+: Adjusted anchored neighborhood regression for fast super-resolution. In Proc. the 12th Asian Conference on Computer Vision, November 2014, pp.111-126.
Dong C, Chen C L, He K, Tang X. Learning a deep convolutional network for image super-resolution. In Proc. the 13th European Conference on Computer Vision, Part IV, September 2014, pp.184-199.
Timofte R, Rothe R, van Gool L. Seven ways to improve example-based single image super resolution. In Proc. the 2016 IEEE Computer Vision and Pattern Recognition, June 2016, pp.1865-1873.
Zontak M, Irani M. Internal statistics of a single natural image. In Proc. the 2011 IEEE Computer Vision and Pattern Recognition, June 2011, pp.977-984.
Glasner D, Bagon S, Irani M. Super-resolution from a single image. In Proc. the 12th IEEE International Conference on Computer Vision, September 2009, pp.349-356.
Park S C, Min K P, Kang M G. Super-resolution image reconstruction: A technical overview. IEEE Signal Processing Magazine, 2003, 20(3): 21-36.
Tsai R Y, Huang T S. Multi-frame image restoration and registration. In Advances in Computer Vision and Image Processing, JAI Press, 1984, pp.317-339.
Yang J, Lin Z, Cohen S. Fast image super-resolution based on in-place example regression. In Proc. the 2013 IEEE Conference on Computer Vision and Pattern Recognition, June 2013, pp.1059-1066.
Huang J B, Singh A, Ahuja N. Single image super resolution from transformed self-exemplars. In Proc. the 2015 IEEE Conference on Computer Vision and Pattern Recognition, June 2015, pp.5197-5206.
Tian Y, Zhou F, Yang W, Shang X, Liao Q. Anchored neighborhood regression based single image super resolution from self-examples. In Proc. the 2016 IEEE International Conference on Image Processing, September 2016, pp.2827-2831.
Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel M L. Single-image super-resolution via linear mapping of interpolated self-examples. IEEE Transactions on Image Processing, 2014, 23(12): 5334-5347.
Barnes C, Zhang F L. A survey of the state-of-the-art in patch-based synthesis. Computational Visual Media, 2017, 3(1): 3-20.
Zhang F L, Wang J, Shechtman E, Zhou Z Y, Shi J X, Hu S M. PlenoPatch: Patch-based plenoptic image manipulation. IEEE Transactions on Visualization and Computer Graphics, 2017, 23(5): 1561-1573.
Arya S, Mount D M, Netanyahu N S, Silverman R, Wu A Y. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of the ACM, 1998, 45(6): 891-923.
Choi J S, Kim M. Single image super-resolution using global regression based on multiple local linear mappings. IEEE Transactions on Image Processing, 2017, 26(3): 1300-1314.
Wu H, Zhang J, Wei Z. High resolution similarity directed adjusted anchored neighborhood regression for single image super-resolution. IEEE Access, 2018, 6: 25240-25247.
Jiang J, Fu J, Lu T, Hu R, Wang Z. Locally regularized anchored neighborhood regression for fast super-resolution. In Proc. the 2015 IEEE International Conference on Multimedia and Expo, June 2015, Article No. 93.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
ESM 1
(PDF 681 kb)
Rights and permissions
About this article
Cite this article
Ding, N., Liu, YP., Fan, LW. et al. Single Image Super-Resolution via Dynamic Lightweight Database with Local-Feature Based Interpolation. J. Comput. Sci. Technol. 34, 537–549 (2019). https://doi.org/10.1007/s11390-019-1925-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11390-019-1925-9