An Improved Low-Cost Adaptive Bilinear Image Interpolation Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 223)


Image scaling is a very important technique and has been widely used in many image and video processing applications. To achieve the goal of low cost and real time, a novel scaling algorithm is proposed which consists of a bilinear interpolation and an adaptive sharpening filter. The proposed sharpening filter is added to perfect the blurring effects existing in traditional bilinear interpolation methods. Simultaneously, we also verify the scaling quality by taking into account the adaptive technology. Compared with the previous bilinear techniques, our method performs better in terms of both quantitative evaluation and visual quality.


Adaptive technology Scaling Sharpening filter 


  1. 1.
    Chen S-L, Huang H-Y, Luo C-H (2011) A low-cost high-quality adaptive scalar for real-time multimedia applications. IEEE Trans Circuits Syst 21(11):1600–1611Google Scholar
  2. 2.
    Meijering E-H-W, Zuiderveld K-J, Viergever M–A (1999) Image reconstruction by convolution with symmetrical piecewise nth-order polynomial kernels. IEEE Trans Image Process 8(2):192–201MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Caselles V, Morel J-M, Sbert C (1998) An axiomatic approach to image interpolation. IEEE Trans Image Process 7(3):376–386MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Angelopoulou ME, Bouganis C-S, Cheung PYK, Constantinides GA (2009) Robust real-time super-resolution on FPGA and an application to video enhancement. ACM Trans Reconfigurable Technol Syst 2:1-29Google Scholar
  5. 5.
    Bowen O, Bouganis C-S (2008) Real-time image super resolution using an FPGA. In: Field programmable logic and applications 2008 FPL International Conference on vol 9, pp 89–94Google Scholar
  6. 6.
    Jensen K, Anastassiou D (1995) Subpixel edge localization and the interpolation of still images. IEEE Trans Image Process 4(3):285–295CrossRefGoogle Scholar
  7. 7.
    Keys R-G (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Ridella S, Rovetta S, Zunino R (2000) Iavq-Interval-arithmetic vector quantization for image compression. IEEE Trans Circuits Syst Part II 47(12):1378–1390CrossRefGoogle Scholar
  9. 9.
    Wang Q, Ward R (2001) A new edge-directed image expansion scheme. Proc Int Conf Image Process 3:899–902Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhiyong Pang
    • 1
  • Huimin Dai
    • 1
  • Hongzhou Tan
    • 1
  • Dihu Chen
    • 1
  1. 1.School of physics and engineeringSun Yat-sen UniversityGuangzhouPeople’s Republic of China

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