Super-resolution restoration algorithm based on contourlet transform

The image super-resolution reconstruction algorithm based on wavelet transform is widely used in almost all applications of image restoration such as image coding, image enhancement, image drying, image fusion and so on. But the wavelet transform can only express the location and characteristics of singularity, and is powerless for the feature of the higher dimension. In addition, the wavelet transform kernel lacks the directivity, and can not obtain the geometric smoothness of the contour. In view of this kind of problem, this paper proposes a super-resolution reconstruction algorithm based on Contourlet transform. The algorithm extends the advantages of wavelet to the high-dimensional space, which can better describe the characteristics of the high-dimensional space, and be more suitable for dealing with information with hyperplane singularity. It is concluded that the improved algorithm has a good effect on both the subjective visual effect and the aspect of PSNR, especially in the recovery of the detailed information of the image, by the experimental simulation and compared with other algorithms.