Wavelet-domain LS-based Image Superresolution
基于小波域LS方法的图象超分辨率重构算法

This paper proposes a wavelet-domain Least-square (LS) based algorithm for image superresolution. Beginning with presenting the edge models popularly accepted in the literature, it is demonstrated in this paper that the edges in different scales are similar to each other in form. This property is called the self-similarity of the multiscale edges. Due to the property, it is possible to predict the three subbands of wavelet coefficients. In order to guarantee the stability and effectiveness of the prediction, the least-square method is adopted. The wavelet coefficients obtained so far are not correct where the multiscale edges are not self-similar. So, the correlation correction method is used to reduce the kind of distortion. Once the wavelet coefficients are obtained, the high resolution image can be reconstructed. Because the algorithm properly preserves the geometrical regularity around the edges, the induced image is of high visual quality. Besides, since only the wavelet coefficients near edges need to be predicted, the algorithm is computationally efficient. Simulations demonstrate the performance of the method.