基于双阈值Huber范数估计的图像正则化超分辨率算法

多帧图像超分辨率算法利用图像间的互补信息, 可以从一系列具有亚像素位移的低分辨率影像数据中重建出高分辨率图像. 在众多超分辨率算法中, 正则化方法以其求解病态问题的有效性而被广泛应用, 但在此类方法中, 最优估计算子的估计准确度对最后的重建结果有着较大的影响. 本文在现有正则化超分辨率重建算法的基础上, 提出了一种基于双阈值Huber范数的极大似然估计算子, 可以提高Huber范数对于阈值取值的容忍性和算子估计精度; 并给出了基于该算子的正则化超分辨率算法的迭代公式. 通过对仿真图像进行重建, 结果表明算法可有效地抑制各种噪声并保证重建效果; 同时将此算法应用于实际图像的超分辨率重建, 有效地提高了目标影像的空间分辨率. 关键词： 图像处理 图像重建 超分辨率 正则化

A regularized super resolution algorithm based on the double threshold Huber norm estimation

Multi-frame super-resolution reconstruction is a technology which obtains a high-resolution image from several low-resolution images of the same scene. Among various super resolution methods, the regularized method is widely used since it has advantages for solving the ill-posed problems. However, the super-resolution reconstruction results based on this method strongly depend on the estimation accuracy of the optimum estimator. In this paper, a double-threshold Huber norm based maximum likehood estimator is proposed, which improves the threshold tolerance of the estimator and increases the estimation accuracy. Then a regularized algorithm based on this estimator is presented. The super-resolution reconstruction results of synthetic low resolution images confirm that the proposed algorithm has better performance over the existing algorithms. The proposed algorithm is also used to deal with the low-resolution images obtained from a plenoptic camera. The results confirm the effectiveness of the proposed algorithm.
Keywords： image processing image reconstruction superresolution regularization