Skip to main content
Springer Nature Link
Log in

Color Image Super-Resolution and Enhancement with Inter-Channel Details at Trivial Cost

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Image super-resolution is essential for a variety of applications such as medical imaging, surveillance imaging, and satellite imaging, among others. Traditionally, the most popular color image super-resolution is performed in each color channel independently. In this paper, we show that the super-resolution quality can be further enhanced by exploiting the cross-channel correlation. Inspired by the High-Quality Linear Interpolation (HQLI) demosaicking algorithm by Malvar et al., we design an image super-resolution scheme that integrates intra-channel interpolation with cross-channel details by isotropic linear combinations. Despite its simplicity, our super-resolution method achieves the accuracy comparable with the existing fastest state-of-the-art super-resolution algorithm at 20 times faster speed. It is well applicable to applications that adopt traditional interpolations, for improved visual quality at trivial computation cost. Our comparative study verifies the effectiveness and efficiency of the proposed super-resolution algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Greenspan H. Super-resolution in medical imaging. The Computer Journal, 2009, 52(1): 43-63.

    Google Scholar 

  2. Zou W W W, Yuen P C. Very low resolution face recognition problem. IEEE Transactions on Image Processing, 2012, 21(1): 327-340.

    MathSciNet  MATH  Google Scholar 

  3. Thornton M W, Atkinson P M, Holland D A. Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixelswapping. International Journal of Remote Sensing, 2006, 27(3): 473-491.

    Google Scholar 

  4. 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 (JCST), 2018, 33(3): 502-510.

    MathSciNet  Google Scholar 

  5. Glasbey C A, Mardia K V. A review of image-warping methods. Journal of Applied Statistics, 1998, 25(2): 155-171.

    MATH  Google Scholar 

  6. Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution. IEEE Computer Graphics and Applications, 2002, 22(2): 56-65.

    Google Scholar 

  7. 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.

    MathSciNet  MATH  Google Scholar 

  8. Yang J, Wang Z, Lin Z et al. Coupled dictionary training for image super-resolution. IEEE Trans. Image Processing, 2012, 21(8): 3467-3478.

    MathSciNet  MATH  Google Scholar 

  9. Kim K I, Kwon Y. Single-image super-resolution using sparse regression and natural image prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(6): 1127-1133.

    Google Scholar 

  10. Ding N, Liu Y P, Fan L W et al. Single image superresolution via dynamic lightweight database with localfeature based interpolation. JCST, 2019, 34(3): 537-549.

    Google Scholar 

  11. Dong C, Loy C C, He K, Tang X. Learning a deep convolutional network for image super-resolution. In Proc. the 13th ECCV, September 2014, pp.184-199.

  12. Kim J, Lee K J, Lee M K. Deeply-recursive convolutional network for image super-resolution. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp.1637-1645.

  13. Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 2017, pp.1132-1140.

  14. Ledig C, Theis L, Huszar F et al. Photo-realistic single image super-resolution using a generative adversarial network. In Proc. the 2017 IEEE CVPR, July 2017, pp.105-114.

  15. Haris M, Shakhnarovich G, Ukita N. Deep back-projection networks for super-resolution. In Proc. the 2018 IEEE CVPR, June 2018, pp.1664-1673.

  16. Liu S, Gang R, Li C, Song R. Adaptive deep residual network for single image super-resolution. Computational Visual Media, 2019, 5(4): 391-401.

    Google Scholar 

  17. Zitová B, Flusser J. Image registration methods: A survey. Image and Vision Computing, 2003, 21(11): 977-1000.

    Google Scholar 

  18. Thurnhofer S, Mitra S K. Edge-enhanced image zooming. Optical Engineering, 1996, 35(7): 1862-1870.

    Google Scholar 

  19. Li X, Orchard M T. New edge-directed interpolation. IEEE Trans. Image Processing, 2001, 10(10): 1521-1527.

    Google Scholar 

  20. Glasner D, Bagon S, Irani M. Super-resolution from a single image. In Proc. the 12th ICCV, October 2009, pp.349-356.

  21. Giachetti A, Asuni N. Real-time artifact-free image upscaling. IEEE Trans. Image Processing, 2011, 20(10): 2760-2768.

    MathSciNet  MATH  Google Scholar 

  22. Zhang Y, Fan Q, Bao F et al. Single-image super-resolution based on rational fractal interpolation. IEEE Transactions on Image Processing, 2018, 27(8): 3782-3797.

    MathSciNet  MATH  Google Scholar 

  23. Lian H. Variational local structure estimation for image super-resolution. In Proc. the 2006 International Conference on Image Processing, October 2006, pp.1721-1724.

  24. Morse B S, Schwartzwald D. Image magnification using level-set reconstruction. In Proc. the 2001 IEEE CVPR, December 2001, pp.333-340.

  25. Mousavi H S, Monga V. Sparsity-based color image super resolution via exploiting cross channel constraints. IEEE Trans. Image Processing, 2017, 26(11): 5094-5106.

    MathSciNet  MATH  Google Scholar 

  26. Dai S, Han M, Xu W et al. SoftCuts: A soft edge smoothness prior for color image super-resolution. IEEE Trans. Image Processing, 2009, 18(5): 969-981.

    MathSciNet  MATH  Google Scholar 

  27. Su D, Willis P. Image interpolation by pixel-level datadependent triangulation. Computer Graphics Forum, 2004, 23(2): 189-201.

    Google Scholar 

  28. Szeliski R. Computer Vision: Algorithms and Applications. Springer Science & Business Media, 2010.

  29. Malvar H S, He L W, Cutler R. High-quality linear interpolation for demosaicing of Bayer-patterned color images. In Proc. the 2004 IEEE ICASSP, May 2004, pp.485-488.

  30. Niu Y, Ouyang J, Zuo W, Wang F. Low cost edge sensing for high quality demosaicking. IEEE Transactions on Image Processing, 2018, 28(5): 2415-2427.

    MathSciNet  Google Scholar 

  31. Bevilacqua M, Roumy A, Guillemot C et al. Lowcomplexity single-image super-resolution based on nonnegative neighbor embedding. In Proc. BMVC, Sept. 2012.

  32. 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.

  33. Martin D, Fowlkes C, Tal D et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. the 8th ICCV, July 2001, pp.416-425.

  34. Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars. In Proc. the 2015 IEEE CVPR, June 2015, pp.5197-5206.

  35. Timofte R, de Smet V, van Gool L. Anchored neighborhood regression for fast example-based super-resolution. In Proc. the 2013 ICCV, Dec. 2013, pp.1920-1927.

  36. Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Processing, 2004, 13(4): 600-612.

    Google Scholar 

  37. Lissner I, Preiss J, Urban P, Lichtenauer M S, Zolliker P. Image-difference prediction: From grayscale to color. IEEE Transactions on Image Processing, 2013, 22(2): 435-446.

    MathSciNet  MATH  Google Scholar 

  38. Timofte R, Agustsson E, van Gool L et al. NTIRE 2017 challenge on single image super-resolution: Methods and results. In Proc. IEEE CVPRW, July 2017, pp.1110-1121.

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Chuang-Ye Zhang, Yan Niu & Xi-Ming Li

  2. Key Laboratory of Symbolic Computer and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Yan Niu & Xi-Ming Li

  3. School of Mathematics, Jilin University, Changchun, 130012, China

    Tie-Ru Wu

Authors
  1. Chuang-Ye Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yan Niu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tie-Ru Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xi-Ming Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yan Niu.

Electronic supplementary material

ESM 1

(PDF 445 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, CY., Niu, Y., Wu, TR. et al. Color Image Super-Resolution and Enhancement with Inter-Channel Details at Trivial Cost. J. Comput. Sci. Technol. 35, 889–899 (2020). https://doi.org/10.1007/s11390-020-0272-1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-020-0272-1

Keywords