IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Real-Time Continuous-Scale Image Interpolation with Directional Smoothing

  • Yoo, Yoonjong (Image Processing and Intelligent Systems Laboratory, Department of Advanced Imaging, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University) ;
  • Shin, Jeongho (Department of Web information Engineering Hankyong University) ;
  • Paik, Joonki (Image Processing and Intelligent Systems Laboratory, Department of Advanced Imaging, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University)
  • Received : 2013.11.22
  • Accepted : 2014.03.20
  • Published : 2014.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

A real-time, continuous-scale image interpolation method is proposed based on a bilinear interpolation with directionally adaptive low-pass filtering. The proposed algorithm was optimized for hardware implementation. The ordinary bi-linear interpolation method has blocking artifacts. The proposed algorithm solves this problem using directionally adaptive low-pass filtering. The algorithm can also solve the severe blurring problem by selectively choosing low-pass filter coefficients. Therefore, the proposed interpolation algorithm can realize a high-quality image scaler for a range of imaging systems, such as digital cameras, CCTV and digital flat panel displays.

Keywords

References

  1. T. Blu, P. Thevenaz, and M. Unser, "Linear interpolation revitalized," IEEE Trans. Image Processing, vol. 13, no. 5, pp. 710-719, May 2004. https://doi.org/10.1109/TIP.2004.826093
  2. T. Lehmann, C. Gonner, and K. Spitzer, "Survey: interpolation methods in medical image processing," IEEE Trans. Medical Imaging, vol. 18, no. 11, pp. 1049-1075, November 1999. https://doi.org/10.1109/42.816070
  3. R. Schafer, and L. Rabiner, "A digital signal processing approach to interpolation," Proc. IEEE, vol. 61, no. 6, pp. 692-702, June 1973. https://doi.org/10.1109/PROC.1973.9150
  4. E. Meijering, "A chronology of interpolation: from ancient astronomy to modern signal and image processing," Proc. IEEE, vol. 90, no. 3, pp. 319-342, March 2002. https://doi.org/10.1109/5.993400
  5. M. Unser, "Splines: a perfect fit for signal and image processing," IEEE Signal Processing Magazine, vol. 16, no. 6, pp. 22-38, November 1999. https://doi.org/10.1109/79.799930
  6. S. Jiazheng and S. Reichenbach, "Image interpolation by two-dimensional parametric cubic convolution," IEEE Trans. Image Processing, vol. 15, no. 7, pp. 1857-1870, July 2006. https://doi.org/10.1109/TIP.2006.873429
  7. H. Hsieh and H. Andrews, "Cubic splines for image interpolation and digital filtering," IEEE Trans. Signal Processing, vol. 26, no. 6, pp. 508-571, December 1978. https://doi.org/10.1109/TASSP.1978.1163154
  8. E. Maeland, "On the comparison of interpolation methods," IEEE Trans. Medical Imaging, vol. 7, no. 3, pp. 213-217, September 1988. https://doi.org/10.1109/42.7784
  9. K. Hong, J. Paik, H. Kim, and C. Lee, "An edge preserving image interpolation system for a digital camcorder," IEEE Trans. Consumer Electronics, vol. 41, no. 3, pp. 279-284, August 1996.