Skip to main content
SpringerLink
Log in

Image super-resolution by prediction of dual tree-CWT coefficient at a finer scale

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents an image of Super-Resolution (SR) technique by the construction of DT-CWT coefficients for a larger scale from the information at a smaller scale for all the subbands. The DT-CWT coefficients prediction for each subband of an image at a finer level is based on phase prediction and estimation of the magnitude separately, followed by combining the magnitude and phase. Inverse DT-CWT is taken with the coefficients at a finer level of each subband along with a Low-Resolution (LR) image in place of a low subband to reconstruct a high-resolution image. The proposed technique is applied to various images, including satellite and standard images. The quantitative and visual results have established the superiority of the proposed scheme over conventional and various state-of-the-art techniques.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Al-Shabili A, Taha B, Al-Ahmad H (2015) Super-resolution algorithm for satellite still images. Int Conf Inf Commun Technol Res ICTRC 2015:48–51. https://doi.org/10.1109/ICTRC.2015.7156418

    Article  Google Scholar 

  2. Celik T, Tjahjadi T (2010) Image resolution enhancement using dual-tree complex wavelet transform. IEEE Geosci Remote Sens Lett 7:554–557. https://doi.org/10.1109/LGRS.2010.2041324

    Article  Google Scholar 

  3. Chang SG, Cvetković Z, Vetterli M (2006) Locally adaptive wavelet-based image interpolation. IEEE Trans Image Process 15:1471–1485. https://doi.org/10.1109/TIP.2006.871162

    Article  Google Scholar 

  4. Rivaz P De (2000) Complex Wavelet Based Image Analysis and Synthesis. Dissertation. Trinity College, University of Cambridge

  5. Demirel H, Anbarjafari G (2011) Discrete wavelet transform-based satellite image resolution enhancement. IEEE Trans Geosci Remote Sens 49:1997–2004. https://doi.org/10.1109/TGRS.2010.2100401

    Article  MATH  Google Scholar 

  6. Demirel H, Anbarjafari G (2011) Image resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans Image Process 20:1458–1460. https://doi.org/10.1109/TIP.2010.2087767

    Article  MathSciNet  MATH  Google Scholar 

  7. Fan DP, Cheng MM, Liu JJ, et al (2018). Salient objects in clutter: Bringing salient object detection to the foreground. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 11219 LNCS:196–212. https://doi.org/10.1007/978-3-030-01267-0_12

  8. Frei W (1981) Digital image processing. IEEE Commun Mag 19:53–54

    Article  Google Scholar 

  9. Fu K, Zhao Q, Yu-Hua Gu I, Yang J (2019) Deepside: A general deep framework for salient object detection. Neurocomputing 356:69–82. https://doi.org/10.1016/j.neucom.2019.04.062

    Article  Google Scholar 

  10. Gajjar PP, Joshi MV (2010) New learning based super-resolution: use of DWT and IGMRF prior. IEEE Trans Image Process 19:1201–1213. https://doi.org/10.1109/TIP.2010.2041408

    Article  MathSciNet  MATH  Google Scholar 

  11. Hong SH, Wang L, Truong TK (2018). An improved approach to the cubic-spline interpolation. Proc - Int Conf image process ICIP 1468–1472. https://doi.org/10.1109/ICIP.2018.8451362

  12. Hou HS, Andrews HC (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust 26:508–517. https://doi.org/10.1109/TASSP.1978.1163154

    Article  MATH  Google Scholar 

  13. Kingsbury NG (1998). The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters. Proc 8th IEEE DSP work Utah paper 86

  14. Kumar CNR (2010) A novel and robust wavelet based super resolution reconstruction of low resolution images using efficient Denoising and adaptive interpolation. Int J Image Process 4:401–420

    Google Scholar 

  15. Kumar Maurya S, Kumar Mishra P, Kumar Singh R, Kumar Misra A (2012). Image enhancement by spline interpolation and adaptive power spectrum cut-off of filtered images. In: IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012

  16. Lam EP (2006) An edge directed image interpolation technique based on wavelet preprocessing. IEEE Nucl Sci Symp Conf Rec 2006:2042–2046. https://doi.org/10.1109/NSSMIC.2006.354315

    Article  Google Scholar 

  17. Lehmann TM, Gönner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18:1049–1075. https://doi.org/10.1109/42.816070

    Article  Google Scholar 

  18. Lu X, Hong PS, Smith MJT (2003) An efficient directional image interpolation method. ICASSP, IEEE Int Conf Acoust Speech Signal Process - Proc 3:97–100

    Google Scholar 

  19. Maurya SK, Singh RK, Misra A (2012). Hybrid image restoration using SWT based denoising and regularization in frequency domain. In: ICPCES 2012–2012 2nd International Conference on Power, Control and Embedded Systems

  20. Reeves TH, Kingsbury NG Prediction of coefficients from coarse to fine scales in the complex. Proc IEEE ICASSP 1:508–511

  21. Selesnick IW, Baraniuk RG, Kingsbury NG (2005) The dual-tree complex wavelet transform. IEEE Signal Process Mag 22:123–151. https://doi.org/10.1109/MSP.2005.1550194

    Article  Google Scholar 

  22. Thung KH, Raveendran P (2009). A survey of image quality measures. Int Conf Tech Postgraduates 2009, TECHPOS 2009. https://doi.org/10.1109/TECHPOS.2009.5412098

  23. Vlachos T (2005) Image resolution upscaling in the wavelet domain using directional cycle spinning. J Electron Imaging 14:040501. https://doi.org/10.1117/1.2061247

    Article  Google Scholar 

  24. Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9:81–84. https://doi.org/10.1109/97.995823

    Article  Google Scholar 

  25. Wang D, Ding W, Man Y, Cui L (2010). A joint image quality assessment method based on global phase coherence and structural similarity. Proc - 2010 3rd Int Congr image signal process CISP 2010 5:2307–2311. https://doi.org/10.1109/CISP.2010.5647786

  26. Wang Z, Simoncelli EP (2004). Local phase coherence and the perception of blur. In: Advances in Neural Information Processing Systems

  27. Xie C, Liu Y, Zeng W, Lu X (2019) An improved method for single image super-resolution based on deep learning. Signal, Image Video Process 13:557–565. https://doi.org/10.1007/s11760-018-1382-x

    Article  Google Scholar 

  28. Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15:2226–2238. https://doi.org/10.1109/TIP.2006.877407

    Article  Google Scholar 

  29. Zhao J, Liu JJ, Fan DP, et al (2019). EGNet: Edge guidance network for salient object detection. Proc IEEE Int Conf Comput Vis 2019-Octob:8778–8787. https://doi.org/10.1109/ICCV.2019.00887

  30. Zhao F, Si W, Dou Z (2017). Image super-resolution via two stage coupled dictionary learning. Multimed tools Appl 1–8. https://doi.org/10.1007/s11042-017-5493-0

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, GLA University, Mathura, UP, 281406, India

    Sanjay Kumar Maurya

  2. Department of Electrical Engineering, MNNIT, Prayagraj, UP, 211004, India

    Ravindra Kumar Singh

Authors
  1. Sanjay Kumar Maurya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ravindra Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanjay Kumar Maurya.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maurya, S.K., Singh, R.K. Image super-resolution by prediction of dual tree-CWT coefficient at a finer scale. Multimed Tools Appl 80, 2875–2886 (2021). https://doi.org/10.1007/s11042-020-09843-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09843-0

Keywords

Search

Navigation