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Super-Resolution Algorithm

Super Resolution is an artificial increasing of the actual image resolution. It attempts to reconstruct the original scene that gave rise to the image.

The most frequently used ways to solve this problem are different kinds of image interpolation. Bicubic interpolation is probably the most popular one. The interpolation procedures are presented in many image processing software systems

Interpolation is not a good solution. The problem is that interpolation can not ensure the effective restoration of the highest frequency part of the image spectrum. This means that many of the smallest details and boundaries of a complicated configuration can not be restored by using interpolation. The image obtained by using interpolation as a rule will not be sharp, and often it may be smoothed. This is a significant disadvantage of interpolation.

Super-resolution means not only a formal enlargement of the image. First of all it means extraction of the unknown part of image spectra in the highest frequency domain. Only such a solution ensures preservation of image details and extraction of the smallest details that are invisible on the input image.

Our super-resolution algorithm is based on this approach. It extrapolates the image spectra to the highest frequency domain. Comparison of the images resampled by the interpolation and our super- resolution show that the latter is much more powerful both from the subjective point of view (visual impression) and the objective one. The latter means that the high frequency part of the spectrum corresponding to the interpolated image is very poor, while one corresponding to the image obtained by our super-resolution contains a lot of useful information.
The solution of the super resolution problem is based on the extrapolation of the orthogonal image spectra (Fourier, Cosine or Walsh) to the unknown highest frequency domain. This approach is implemented by using iterative approximation of the unknown high frequency part of the orthogonal spectra, and final correction of the obtained image with increased resolution, using multi-valued filters.

Comparison chart of spectra of the supersampled image to the images that are the results of different interpolation techniques
(red -original, blue - our supersampling, green - interpolation)


Original Image
(click on to see supersampled image,
make sure you don't have pop-ups disabled)


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Last Updated
Mon, November 01, 2009 13:18