Skip to main content
SpringerLink
Log in

New adaptive interpolation scheme for image upscaling

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

Abstract

Preserving edge structures and image details simultaneously is considered the main challenge for image interpolation techniques that produce high-resolution images from their low-resolution counterparts. Two variants of a new adaptive interpolation scheme are proposed in this paper. In the proposed scheme for better interpolation of natural images, a new estimation mechanism that utilizes discontinuities in blocks around missing pixels is devised to discriminate strong edges. Strong edge pixels are obtained by using amended error linear interpolation and cubic convolution interpolation. Adaptive interpolation weights determined by inverse intensity distances in local windows are used to produce non-strong edge pixels based on local image structure. The proposed amended linear interpolation and cubic convolution interpolation exhibited approximately comparable performances. Simulation results on different types of images, including natural, texture, and cartoon images, demonstrate that, compared with other state-of-the-art algorithms, the proposed algorithm can generate better visual quality of the magnified images with higher peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM) index, and reasonable execution time.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. AFD (2014) Free photos.Available from: http://all-free-download.com/free-vector/vector-clip-art/sort-by-newest/page/270/

  2. Amanatiadis A, Andreadis I (2009) A survey on evaluation methods for image interpolation. Meas Sci Technol 20(10):104015

    Article  Google Scholar 

  3. Battiato S, Gallo G, Stanco F (2002) A locally adaptive zooming algorithm for digital images. Image Vis Comput 20(11):805–812

    Article  Google Scholar 

  4. Biancardi A, Cinque L, Lombardi L (2002) Improvements to image magnification. Pattern Recogn 35(3):677–687

    Article  MATH  Google Scholar 

  5. Cha Y, Kim S (2007) The error-amended sharp edge (EASE) scheme for image zooming. IEEE Trans Image Process 16(6):1496–1505

    Article  MathSciNet  Google Scholar 

  6. Chen M-J, Huang C-H, Lee W-L (2005) A fast edge-oriented algorithm for image interpolation. Image Vis Comput 23(9):791–798

    Article  Google Scholar 

  7. Chen H-Y, Leou J-J (2010) Saliency-directed image interpolation using particle swarm optimization. Signal Process 90(5):1676–1692

    Article  MATH  Google Scholar 

  8. CIPR (2014) Test images. Available from: http://www.cipr.rpi.edu/resource/stills/kodak.html

  9. CVG (2014) Test images. [cited 02/04/2014; Available from: http://decsai.ugr.es/cvg/index2.php.

  10. Freedman G, Fattal R (2011) Image and video upscaling from local self-examples. ACM Trans Graph (TOG) 30(2):12

    Article  Google Scholar 

  11. Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56–65

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  13. Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: Computer Vision, 2009 I.E. 12th International Conference on. IEEE

  14. Han J-W et al (2010) A novel image interpolation method using the bilateral filter. IEEE Trans Consum Electron 56(1):175–181

    Article  Google Scholar 

  15. Irani M, Peleg S (1993) Motion analysis for image enhancement: resolution, occlusion, and transparency. J Vis Commun Image Represent 4(4):324–335

    Article  Google Scholar 

  16. Jing M, Wu J (2013) Fast image interpolation using directional inverse distance weighting for real-time applications. Opt Commun 286:111–116

    Article  Google Scholar 

  17. Jurio A et al (2011) Image magnification using interval information. IEEE Trans Image Process 20(11):3112–3123

    Article  MathSciNet  Google Scholar 

  18. Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160

    Article  MathSciNet  MATH  Google Scholar 

  19. Kim H, Cha Y, Kim S (2011) Curvature interpolation method for image zooming. IEEE Trans Image Process 20(7):1895–1903

    Article  MathSciNet  Google Scholar 

  20. Lee YJ, Yoon J (2010) Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans Image Process 19(10):2682–2692

    Article  MathSciNet  Google Scholar 

  21. Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527

    Article  Google Scholar 

  22. Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055

    Article  Google Scholar 

  23. Mallat S, Yu G (2010) Super-resolution with sparse mixing estimators. IEEE Trans Image Process 19(11):2889–2900

    Article  MathSciNet  Google Scholar 

  24. OBT (2014) Original brodatz texture database. [cited 2014 12-4-2014]; Available from: http://multibandtexture.recherche.usherbrooke.ca/original_brodatz.html

  25. Sajjad M, Ejaz N, Baik SW (2012) Multi-kernel based adaptive interpolation for image super-resolution. Multimedia Tools Appl 1–23

  26. Sajjad M, Khattak N, Jafri N (2007) Image magnification using adaptive interpolation by pixel level data-dependent geometrical shapes. Int J Comput Sci Eng 1(2):118–127

    Google Scholar 

  27. Shi G et al (2010) Context-based adaptive image resolution upconversion. J Electron Imaging 19(1):013008–013008-9

    Article  Google Scholar 

  28. SIPI (2014) SIPI image database. [cited 06/04/2014; Available from: http://sipi.usc.edu/database

  29. 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

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  31. Wang Z et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  32. Wittman T (2005) Mathematical Techniques for Image Interpolation. Department of Mathematics University of Minnesota

  33. Wong C-S, Siu W-C (2010) Adaptive directional window selection for edge-directed interpolation. In: Computer Communications and Networks (ICCCN), 2010 Proceedings of 19th International Conference on. IEEE

  34. Yan X, Shen J (2010) Fast gradient-aware upsampling for cartoon video. In: Image Analysis and Signal Processing (IASP), 2010 International Conference on. IEEE

  35. Yang J et al (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873

    Article  MathSciNet  Google Scholar 

  36. Yun Y, Bae J, Kim J (2012) Multidirectional edge-directed interpolation with region division for natural images. Opt Eng 51(4):040503-1–040503-3

    Article  Google Scholar 

  37. Zhang D, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238

    Article  Google Scholar 

  38. Zhang X, Wu X (2008) Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation. IEEE Trans Image Process 17(6):887–896

    Article  MathSciNet  Google Scholar 

  39. Zhang L, Zhang D, Mou X (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  Google Scholar 

  40. Zhou D, Shen X, Dong W (2012) Image zooming using directional cubic convolution interpolation. IET Image Process 6(6):627–634

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This study was partially supported by the Research University Grant for Individual (RUI), Universiti Sains Malaysia, Malaysia titled “Development of an intelligent auto-Immune Diseases Diagnostic System by classification of Hep_2 Immunofluorescence Patterns”.

Author information

Authors and Affiliations

  1. Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia

    Suheir M. Elbayoumi Harb & Nor Ashidi Mat Isa

  2. Computer Department, Palestine Technical College, Gaza, Palestine

    Samy Salamah

Authors
  1. Suheir M. Elbayoumi Harb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nor Ashidi Mat Isa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samy Salamah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nor Ashidi Mat Isa.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harb, S.M.E., Isa, N.A.M. & Salamah, S. New adaptive interpolation scheme for image upscaling. Multimed Tools Appl 75, 7293–7325 (2016). https://doi.org/10.1007/s11042-015-2647-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2647-9

Keywords

Search

Navigation