一种基于图像配准的超分辨率重建方法

为了提高配准算法的精度并改善超分辨率重建图像的质量，提出了一种具有子像素级精度的图像配准方法。首先，对基于三参数模型的图像配准方法进行介绍并优化。然后，提出了基于四参数模型的图像配准方法，由于没有直接将旋转角度参量引入到运动模型中，有效地避免了原方法中泰勒级数展时的小角度假设。最后，根据配准算法所得到的子像素级运动信息，采用迭代反投影算法进行超分辨率重建。实验结果表明：该配准算法取得了更高的精度，平均平移误差减少0.0297像素 ,平均旋转角度误差减少0.3564度；重建图像具有更好的视觉效果，平均PSNR值提高0.75dB。可以被广泛地应用于相互间主要存在平移和大角度旋转的多幅低分辨率图像的大倍数超分辨率重建，满足了实际应用要求。

A super-resolution reconstruction method based on Image registration

In order to　improve the precision of the registration algorithm and the quality of the super-resolution reconstructed image, an image registration method with sub-pixel precision is proposed. First, the image registration method based on three-parameter model is introduced and optimized. Then, the image registration algorithm based on four-parameter model is proposed. Because the rotation angle is not directly included in the movement model, the hypothesis of small rotation angle while Taylor series expanding in the original method is avoided effectively. Finally, according to the movement information with sub-pixel precision gained by these two registration algorithms respectively, super-resolution reconstruction is performed by the adoption of iterative back projection algorithm. Experimental results indicate that this image registration algorithm achieves higher precision. The average translation error is reduced 0.0297 pixels and the average rotation angle error is decreased 0.3564 degree. The reconstructed image has better visual effect and the average PSNR is increased 0.75dB. It can be extensively used in the large multiple super-resolution reconstruction of low-resolution images from which differs mostly by translation and big rotation, which satisfies the requirements of practical application.