具有随机位移的多帧图像超分辨重建快速算法

常用的多帧图像超分辨重建算法大都基于图像恢复原理，因此计算量大，难以在线实时实现．为此，针对稀疏采样以及具有随机位移情况下的多帧图像超分辨重建问题，提出了三种融合重建快速算法，即邻域组合插值(NCI)方法、邻域扩展插值(NEI)方法以及全局双向线性插值(GTDLI)方法．仿真实验结果表明了三种算法的有效性，而且相对于经典的图像超分辨重建算法具有明显的速度优势．最后通过分析分别给出了三种算法所适合的应用场合．

Fast super-resolution reconstruction algorithms for multi-frame images with random shifts

The traditional multi-frame super-resolution(SR) reconstruction methods are computationally expensive and only applicable to offline processing cases.To reduce the workload of SR fusion for real-time applications,three algorithms,i.e.,neighborhood combination interpolation(NCI),neighborhood expansion interpolation(NEI) and global two-direction linear interpolation(GTDLI),are proposed in this paper under the condition of multiple sparsely-sampled lower resolution images with global random shifts.Simulation results illustrate the effectiveness of these proposed algorithms and the advantages in speed over classic SR algorithms.Finally,the applicable fields for the three algorithms are concluded through theoretical analysis and experimental comparison.