一种新的图像配准和超分辨率重建算法

对低分辨率观测图像进行精确配准是实现图像超分辨率重建的关键。然而，当低分辨率图像中的混叠分量达到一定程度时，许多配准算法不能满足超分辨率重建的精度要求。将图像配准和超分辨率重建联合实现的方法受混叠影响较小，该文分析其原因并提出实现这类方法的新算法，该算法采用类似于变量投影的思想，改善问题求解的条件，从而克服常用的坐标轮转下降法的一些不足。新算法利用Lanczos方法和Gauss求积原理高效地实现，并且能够处理低分辨率图像之间平移和旋转等形式的运动。实验结果证明了该方法的有效性。

A New Algorithm for Image Registration and Super-Resolution Reconstruction

The accurate knowledge of the sub-pixel registration parameters for each observed Low-Resolution (LR) image is the key for image Super-Resolution Reconstruction (SRR). Many registration algorithms fail to provide sufficient precision for SRR when the LR images are severely aliased. However, the method which combines the registration problem into SRR can obtain accurate estimation for registration parameters of each LR image in this situation. This paper analyzed the method and proposed a new approach to solve it. The proposed approach uses the principle similar to variable projection which results in a better-conditioned problem and avoids some shortcomings of cyclic coordinate descent optimization procedure. It can be efficiently implemented by using Lanczos method and Gauss quadrature theory. As a result, the proposed approach can deal with translation and rotation between the LR images. Experimental results demonstrate the effectiveness of our approach.