Fast Algorithms of Image Fusion for Super-Resolution Reconstruction from Multiple Images with Random Shifts

The commonly-used image Superresolution (SR) reconstruction methods are computationally expensive and only applicable to “offline processing” cases. To reduce the work-load of SR fusion, three algorithms, i.e., Neighborhood searching nearest interpolation (NSNI), Fixed-distance searching nearest interpolation (FDSNI) and Two-direction linear interpolation (TDLI), are proposed in this paper in the condition of multiple lower resolution images with global random shifts. The simulation results illustrate the effectiveness, speed and quality advantages of the three proposed SR algorithms over classic algorithms.