# An Algorithm of Digital Image Interpolation

## Keywords:

interpolation, orthogonal transform, image resizing algorithm## Abstract

The effective algorithms for reducing and increasing the size of images using the orthogonal wavelet-like transformation are presented. The experimental results show an improvement in terms of PSNR in comparison to the well-known image interpolation algorithms.

## References

J. Mukherjee, S. K. Mitra, “Image resizing in the compressed domain using subband DCT,” IEEE Trans. Circuits and Systems for Video Technology, vol. 12, no. 7, pp. 620-627, 2002.

S. A. Martucci, “Image resizing in the discrete cosine transform domain,” IEEE Proc. Int. Conf. Image Processing, vol. 2, pp. 244-247, 1995.

J. Scott, R. Tutwiler and M. Pusateri, “Hyper-spectral content aware resizing,” IEEE Conf. Applied Imagery Pattern Recognition Workshop, pp. 1-7, 2008.

S. Khachatryan, L. Minasyan, “On synthesis and application of an orthogonal discrete transform for image compression,” Journal of Engineering Academy of Armenia, T4, № 1. Yerevan, pp.124-129, 2007 (in Russian).

S. Khachatryan and L. Minasyan, “Design of orthogonal transform on partion of unity method and their application to image compression, “ Int. Workshop on Local and Non- Local Approximation in Image Processing, LNLA’2008, Tuusula, Finland, pp. 145-152, 2008.

S. Khachatryan and L. Minasyan, “On algorithms of digital image interpolation,” Mathema tics in high School, T. 6, № 2, pp. 32-41, 2010.

R. Gonzalez and R. Woods, Digital Image Processing, 2nd Ed. Upper Saddle River, New Jersey: Prentice-Hall, Inc., 2002.

T. W. Parks and C. S. Burrus. Digital Filter Design. New York: John Wiley & Sons, pp. 209-213, 1987.

A. V. Oppenheim and R. W. Schafer. “Discrete-time signal processing”. Upper Saddle River, NJ: Prentice-Hall, pp. 450-454, 1999.

R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. on Signal Processing, Acoustics, Speech, and Signal Processing 29 (6), pp.1153-1160, 1981.

K. Turkowski and S. Gabriel, “Filters for common resampling Tasks”. In Andrew S. Glassner. Graphics Gems I. Academic Press. pp. 147–165. ISBN 9780122861659, 1990.

W. Burger, M. J. Burge, Principles of digital image processing: core algorithms. Springer. pp. 231–232. ISBN 9781848001947, 2009.

G. Schaub, “Genuine Fractals 4.1; Resampling with GF might make the megapixel race moot,” March, Shutterbug, 2006.

J. Canny, “A computational approach to edge detection,” IEEE Transactions on PAMI, 8(6), pp. 679–698, 1986.

## Downloads

## Published

## How to Cite

*Mathematical Problems of Computer Science*,

*37*, 45–52. Retrieved from http://mpcs.sci.am/index.php/mpcs/article/view/251

## Issue

## Section

## License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.