| Image super-resolution has become an active research area. It refers to as image processing algorithms to reconstruct high-resolution(HR) images. Super-resolution technology has been widely used in remote sensing, military surveillance and medical diagnosis, etc. Currently, super resolution technique of 3D surface textures is an less studied research field.As a time-frequency analysis tool, wavelet analysis is important in image processing applications. Wavelet analysis for super-resolution is an active research topic in the field. Wavelet transform has good properties such as multi-resolution and time frequency localization, but it has only limited direction that cannot capture the direction information of the image properly. Contourlet transform is developed based on wavelet transform, and it not only inherits the advantages of wavelet transform, but has multi directional and anisotropy. Therefore it is promising to image super-resolution processing since it can capture significant information from a natural image.Based on the previous super resolution methods for 3D surface textures, this paper proposes two new super resolution methods. One is based on wavelet transform and developed for many types of 3D surface textures with similar texture, by using wavelet transform and textons substitution. Finally a set of experiments and results are produced to show the evidences of the efficiency of the algorithm.To overcome the weaknesses of wavelet transform, the other algorithm is proposed based on contourlet transform. It has a general training set by studying the correlation of contourlet coefficients and combining the SVM theories. This algorithm makes the super resolution of 3D surface textures easier and more practical. Finally, the super-resolution results produced by different methods are evaluated. This new algorithm can effectively maintain the edges information and outperforms other methods in terms of entropy, definition and visual qualities. |
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