为了能够有效地重构出高分辨率图象，提出了一种基于小波域最小二乘法(LS)的图象超分辨率重构算法．该算法是利用多尺度边缘的自相似性，由低分辨率图象通过预测来得到高分辨率图象小波变换的3个高频通道，以实现图象超分辨率重构．由于该算法保持了图象边缘附近的几何正则性，因而能够重构出较高质量的图象．同时，由于小波系数的预测只在边缘处进行，因此该算法具有较小的计算复杂度．实验表明，该算法较好地实现了图象超分辨率重构．

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.