Image quality improvement by method of spatial spectrum extrapolation

In this research we explore methods of image quality improvement using spatial spectral representation and describe two formulations of the problem. In the first one the information on high-resolution details is available from the additional reference image. High-resolution image is obtained by combining the spectra of the source and reference images. In the second formulation additional information is not available. High-resolution image is synthesized by analytic continuation of the source image spectrum. The findings of the research are given.

References

[1] Bikkenin R.R., Chesnokov M.N. Teoriya elektricheskoy svyazi [Theory of Electrical Communication]. Moscow, Akademiya Publ., 2010. 329 p.

[2] Getreuer P. Linear Methods for Image Interpolation. Image Processing On Line, 2011.

[3] Turkowski K., Gabriel S. Filters for Common Resampling Tasks. Boston, Graphics Gems I. Academic Press, 1990, pp. 147-165.

[4] Tsurkov V.I. An analytical model of edge protection under noise suppression by anisotropic diffusion. J. Computer and Systems Sciences International, 2000, vol. 39, no. 3, pp. 437-440.

[5] Tsurkov V.I., Kovkov D.V. Sposob udaleniya shuma v izobrazhenii [Method of Removing Noise in the Image]. Patent RF no. RUS 2316816, 25.08.2005.

[6] Mironov A.A., Tsurkov V.I. Approximation and decomposition by extremal graphs. Computational Mathematics and Mathematical Physics, 1993, vol. 33, no. 2, pp. 251-262.

[7] Mironov A.A., Tsurkov V.I. Network models with fixed parameters at the communication nodes. II. J. Computer and Systems Sciences International, 1994, vol. 32, no. 6, pp. 1-11.

[8] Mironov A.A., Tsurkov V.I. Transport and network problems with the minimax criterion. Computational Mathematics and Mathematical Physics, 1995, vol. 35, no. 1, pp. 15-30.

[9] Bondur V.G. Phase-Spectral Method’s Modeling of Two-Dimension Stochastic Brightness Field, Formed at the Airspace Apparatus Entrance. Issled. Zemli iz kosmosa [Izvestiya. Atmospheric and Oceanic Physics Earth Observation and Remote Sensing], 2000, no. 5, pp. 28-44 (in Russ.).

[10] Zomet A., Peleg S. Multi-sensor super-resolution. Proc. 6th IEEE Workshop Applications of Computer Vision, 2002, pp. 27-31.

[11] Matveev I.A., Murynin A.B. Principles of Development of a Stereovision System for Motion Control of a Robot. Journal of Computer and Systems Sciences International, 2003, vol. 42, no. 3, p. 490.

[12] Matveev I.A., Murynin A.B. Identification of Objects on the Basis of Stereo Images: Optimization of Algorithms for Reconstruction of a Surface. Journal of Computer and Systems Sciences International, 1998, vol. 37, no. 3, p. 487.

[13] Soyfer V.A., ed. Metody komp’yuternoy obrabotki izobrazheniy [Methods of Computer Image Processing]. Moscow, Fizmatlit Publ., 2003.

[14] Gao Y., Rehman A., Wang Z. CW-SSIM Based image classification. 18th IEEE International Conference on Image Processing (ICIP). 2011. IEEE, 2011, pp. 1249-1252.

[15] Greenspan H., Anderson C.H., Akber S. Image enhancement by nonlinear extrapolation in frequency space. IEEE Transactions on Image Processing, 2000, vol. 9, no. 6, pp. 1035-1048.

[16] Khonina S.N., Baranov V.G., Kotlyar V.V. Spectral Method for the Digital Image Fragment Increasing. Komp’yuternaya optika [Computer Optics], 1999, vol. 19, pp. 165-173 (in Russ.).

[17] Alparone L. et al. Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data-fusion contest. IEEE Transactions on Geoscience and Remote Sensing, 2007, vol. 45, no. 10, pp. 3012-3021.