考虑亚像素配准误差的超分辨率图像复原

超分辨率图像复原作为第二代图像复原方向,已成为目前国际图像复原界的一个研究热点.一般来说,超分辨率图像复原是一个病态问题,可以结合图像的先验信息,使其成为良态的,这需要有效的规整化算法.但是,规整化参数的选择多数情况是通过经验确定的,且现有的一些计算规整化参数的方法又过于繁琐.本文讨论了亚像素配准误差引入的情况下噪声的统计模型,利用Miller规整的思想给出了简易可行的规整化参数计算方法.这种规整化参数计算方法能够自适应地根据配准误差和观测噪声局部调整由于配准误差导致的失真.仿真结果表明得到的规整化参数能使规整化算法有效收敛.

Super-Resolution Image Restoration Considering Sub-pixel Registration Error

As the second image restoration,super-resolution image restoration has become an active research issue in the field of image restoration.In general,super-resolution image restoration is an ill-posed problem.Prior knowledge about the image can be combined to make the problem well-posed,which contributes to some regularization methods.In these regularization methods,however,regularization parameter was selected by experience in some cases.Other techniques to compute the parameter had too heavy computation cost.In this paper,owing to the introduction of sub-pixel registration error,the statistic model of the error was discussed.And a simple and available method to solve regularization parameter was proposed in term of Miller's regularization.The method to solve regularization parameter can adaptively and locally regulate the distortion introduced by registration error.Simulations demonstrated that the regularization parameter solved by the method could make super-resolution image restoration convergence more efficiently.