Digital Image Magnification Using Gaussian-Edge Directed Interpolation

  • Muhammad Sajjad
  • Ran Baik
  • Sung Wook Baik
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 215)


This paper presents a simple and cost effective approach for digital image magnification (DIM). DIM is used in various applications and is an enthusiastic area of research at present. The proposed technique uses Gaussian edge directed interpolation to determine the precise weights of the neighboring pixels. The standard deviation of the interpolation window determines the value of ‘σ’ for generating Gaussian kernels. Gaussian kernels preserve the original detail of the low-resolution image to produce high-resolution image of high visual quality. The experimental results show that the proposed technique is superior to other techniques qualitatively as well as quantitatively.


Digital image magnification Gaussian kernel Gaussian sigma Weighted interpolation 



This research is supported by, (1) The Industrial Strategic technology development program, 10041772, (The Development of an Adaptive Mixed-Reality Space based on Interactive Architecture) funded by the Ministry of Knowledge Economy (MKE, Korea), and (2) The MKE (The Ministry of Knowledge Economy), Korea, under IT/SW Creative research program supervised by the NIPA (National IT Industry Promotion Agency)” (NIPA-2012- H0502-12-1013).


  1. 1.
    Jurio A, Pagola M, Mesiar R, Beliakov G, Bustince H (2011) Image magnification using interval information. IEEE Trans Image Process, 20(11):3112–3123Google Scholar
  2. 2.
    Amanatiadis A, Andreadis I (2009) A survey on evaluation methods for image interpolation. Meas Sci Technol 20(10):104015–104021Google Scholar
  3. 3.
  4. 4.
    Yeon JL, Jungho Y (2010) Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans Image Process 19(10):2682–2692Google Scholar
  5. 5.
    Gonzalez RC, Woods RE (2007) Digital image processing, 3rd edn. AmazonGoogle Scholar
  6. 6.
    Shapiro LG, Stockman GC (2001) Computer vision. AmazonGoogle Scholar
  7. 7.
    Hou HS, Andrews HC (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Proc 26:508–517CrossRefMATHGoogle Scholar
  8. 8.
    Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10:1521–1527CrossRefGoogle Scholar
  9. 9.
    Tam WS, Kok CW, Siu WC (2010) A modified edge directed interpolation for images. J Electron Imaging 19(1):1–20CrossRefGoogle Scholar
  10. 10.
    Wittman T (2005) Mathematical techniques for image interpolation. Department of Mathematics, University of MinnesotaGoogle Scholar
  11. 11.
    Lee YJ, Yoon J (2010) Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans Image Process 19(10):2682–2692MathSciNetCrossRefGoogle Scholar
  12. 12.
    Shan Q, Li Z Jia J, Tang CK (2008) Fast image/video upsampling. ACM Transactions on Graphics (SIGGRAPH ASIA) 27:153–160Google Scholar
  13. 13.
    Hung KW, Siu WC (2009) New motion compensation model via frequency classification for fast video super-resolution. IEEE Int Conf Image ProcessGoogle Scholar
  14. 14.
    Baker S, Kanade T (2002) Limits on super-resolution and how to break them. IEEE Trans on Pattern Anal Mach Intell 24:1167–1183Google Scholar
  15. 15.
    Irani M, Peleg S (1993) Motion analysis for image enhancement: resolution, occlusion and transparency. J Vis Commun Image Represent 4(4):324–335CrossRefGoogle Scholar
  16. 16.
    Mallat S, Yu G (2010) Super-resolution with sparse mixing estimators. IEEE Trans Image Process 19(11):2889–2900MathSciNetCrossRefGoogle Scholar
  17. 17.
    Gajjar PP, Joshi MV (2010) New learning based super-resolution: use of DWT and IGMRF prior. IEEE Trans Image Process 19(5):1201–1213MathSciNetCrossRefGoogle Scholar
  18. 18.
    Ni KS, Nguyen TQ (2007) Image super resolution using support vector regression. IEEE Trans Image Process 16(6):1596–1610MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873MathSciNetCrossRefGoogle Scholar
  20. 20.
    Kim KI, Kwon Y (2008) Example-based learning for single image super-resolution and JPEG artifact removal. Technical Report 173, Max Planck InstituteGoogle Scholar
  21. 21.
    Ejaz N, Tariq TB, Baik SW (2012) Adaptive key frame extraction for video summarization using an aggregation mechanism. J Vis Commun Image Represent 23(7):1031–1040CrossRefGoogle Scholar
  22. 22.
    He H, Siu WC (2011) Single image super resolution using Gaussian process regression. IEEE Conf Comput Vis Pattern Recognit 449–456Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.College of Electronics and Information EngineeringSejong UniversitySeoulRepublic of Korea
  2. 2.College of BusinessHonam UniversityGwangjuRepublic of Korea

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