关键词: 图像超分辨, 四参数仿射模型, Fourier-Mellin变换, Kriging插值, 核非线性回归（KNR）

Abstract: Motion estimation and reconstruction are the two most important steps in image superresolution reconstruction based on multiframe processing. For this purpose, Fourier-Mellin transform algorithm is one of the most popular approaches which combines the log-polar image coordinate in the frequency spectrum with phase correlation to estimate the motion parameter. However, the estimation precision of phase correlation is limited to pixel level, and in this manuscript it is improved to sub-pixel level to increase the estimation accuracy of rotation and scaling parameter. As for reconstruction algorithm, in comparison with the standard bicubic interpolation which is based on the local information contained in an image, the Kriging interpolation and Kernel Nonlinear Regression（KNR） superresolution algorithms are discussed which are based on the global information. The effectiveness of the motion estimation and the superresolution reconstruction algorithms discussed in the paper is illustrated by some experimental results.

Key words: image superresolution, 4-parameter affine model, Fourier-Mellin transform, Kriging interpolation, Kernel Nonlinear Regression（KNR）