Skip to main content
SpringerLink
Log in

Color Image Super Resolution by Using Cross-Channel Correlation

  • Conference paper
  • First Online:
Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11166))

Included in the following conference series:

  • 3109 Accesses

Abstract

Single image super resolution (SR) aims to reconstruct a high resolution (HR) image from a low resolution (LR) image. However, many SR methods are only designed for the grayscale images. As a result, when dealing with the color images, the cross-channel information is often ignored by those approaches. In this paper, we propose a color image SR method by taking the cross-channel correlation of color images into consideration. In our method, the gradients of the differences between color channels are required to be sparse. In addition, to make our SR framework more robust, the average signal of the three color channels is also enforced to be sparse in the gradient domain. Finally, to solve the optimization problem which includes the cross-channel-correlation-based regularization terms, an efficient algorithm is presented. Experimental results demonstrate the effectiveness of the proposed method quantitatively and visually.

This work was supported in part by Natural Science Foundation (NSF) of China under Grants 61761005 and 61761007, and in part by the NSF of Guangxi under Grants 2016GXNSFAA380154 and 2016GXNSFAA380216.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    As bridge and man in Set14 are gray images, they are excluded from our experiments.

  2. 2.

    The PSNR is measured on all the color channels; the SSIM is calculated on each channel, and the average value of three channels is reported.

References

  1. Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)

    Article  Google Scholar 

  2. Bevilacqua, M., Roumy, A., Guillemot, C., Morel, A.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: British Machine Vision Conference (BMVC), pp. 1–12 (2012)

    Google Scholar 

  3. Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. SIAM Multiscale Model. Simul. 4(2), 490–530 (2005)

    Article  MathSciNet  Google Scholar 

  4. Chang, K., Ding, P.L.K., Li, B.: Single image super-resolution using collaborative representation and non-local self-similarity. Signal Process. 149, 49–61 (2018)

    Article  Google Scholar 

  5. Chang, K., Ding, P.L.K., Li, B.: Single image super resolution using joint regularization. IEEE Signal Process. Lett. 25(4), 596–600 (2018)

    Article  Google Scholar 

  6. Chang, K., Ding, P.K., Li, B.: Color image demosaicking using inter-channel correlation and nonlocal self-similarity. Signal Process.: Image Commun. 39, 264–279 (2015)

    Google Scholar 

  7. Chang, K., Li, B.: Joint modeling and reconstruction of a compressively-sensed set of correlated images. J. Vis. Commun. Image Represent. 33, 286–300 (2015)

    Article  Google Scholar 

  8. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)

    Article  Google Scholar 

  9. Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25

    Chapter  Google Scholar 

  10. Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)

    Article  MathSciNet  Google Scholar 

  11. Dong, W., Zhang, L., Shi, G., Wu, X.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20(7), 1838–1857 (2011)

    Article  MathSciNet  Google Scholar 

  12. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)

    Article  MathSciNet  Google Scholar 

  13. Goldstein, T., Osher, S.: The split Bregman method for \(l1\) regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009)

    Article  MathSciNet  Google Scholar 

  14. Huang, J., Ahuja, A.S.N.: Single image super-resolution from transformed self-exemplars. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5197–5206. IEEE, Boston (2015)

    Google Scholar 

  15. Kim, J., Lee, J., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646–1654. IEEE, Las Vegas (2016)

    Google Scholar 

  16. Mairal, J., Elad, M., Sapiro, G.: Sparse representation for color image restoration. IEEE Trans. Image Process. 17(1), 53–69 (2008)

    Article  MathSciNet  Google Scholar 

  17. Mousavi, H., Monga, V.: Sparsity based super resolution using color channel constraints. In: IEEE International Conference on Image Processing (ICIP), pp. 579–583. IEEE, Phoneix (2016)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  19. Ren, C., He, X., Teng, Q., Wu, Y., Nguyen, T.Q.: Single image super-resolution using local geometric duality and non-local similarity. IEEE Trans. Image Process. 25(5), 2168–2183 (2016)

    Article  MathSciNet  Google Scholar 

  20. Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1), 259–268 (1992)

    Article  MathSciNet  Google Scholar 

  21. Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)

    Article  MathSciNet  Google Scholar 

  22. Timofte, R., Smet, V.D., Gool, L.V.: Anchored neighborhood regression for fast example-based super resolution. In: IEEE International Conference on Computer Vision (ICCV), pp. 1920–1927. IEEE, Sydney (2013)

    Google Scholar 

  23. Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16817-3_8

    Chapter  Google Scholar 

  24. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  25. Xu, Y., Yu, L., Xu, H., Zhang, H., Nguyen, T.: Vector sparse representation of color image using quaternion matrix analysis. IEEE Trans. Image Process. 24(4), 1315–1329 (2015)

    Article  MathSciNet  Google Scholar 

  26. Yang, C.Y., Yang, M.H.: Fast direct super-resolution by simple functions. In: IEEE International Conference on Computer Vision (ICCV), pp. 561–568. IEEE, Sydney (2013)

    Google Scholar 

  27. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  28. Yang, M., Wang, Y.: A self-learning approach to single image super-resolution. IEEE Trans. Multimed. 15(3), 498–508 (2013)

    Article  Google Scholar 

  29. Yu, M., Xu, Y., Sun, P.: Single color image super-resolution using quaternion-based sparse representation. In: IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), pp. 5804–5808. IEEE, Florence (2014)

    Google Scholar 

  30. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27413-8_47

    Chapter  Google Scholar 

  31. Zhang, K., Gao, X., Tao, D., Li, X.: Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process. 21(11), 4544–4556 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Electronic Information, Guangxi University, Nanning, 530004, China

    Kan Chang, Caiwang Mo, Minghong Li, Tianyi Li & Tuanfa Qin

  2. Guangxi Key Laboratory of Multimedia Communications and Network Technologies, Guangxi University, Nanning, 530004, China

    Kan Chang & Tuanfa Qin

Authors
  1. Kan Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Caiwang Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minghong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianyi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tuanfa Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kan Chang .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chang, K., Mo, C., Li, M., Li, T., Qin, T. (2018). Color Image Super Resolution by Using Cross-Channel Correlation. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00764-5_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00763-8

  • Online ISBN: 978-3-030-00764-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation