Multi-frame Image Super-resolution Reconstruction Based on SIFT
Zhang Zhi, Peng Ye, Runsheng Wang · 2009
Accurate sub-pixel image registration is a key problem in image super-resolution reconstruction. Optical flow methods based on pixel feature, which are widely used in image super-resolution reconstruction, are difficult to achieve registration of sub-pixel accuracy for large motion field. This paper considered a robust multi-frame image super-resolution reconstruction method based on SIFT. Firstly, SIFT operator was used to pick up keypoints and their descriptors of input low-resolution image pairs which are to be registered. Then the candidate keypoint pair was selected, outliers were wiped off through RANSAC, and images pair displacement was computed at the basis of assumed transitional geometry constraint model. Secondly, initial reference frame was selected from vision center frame or specified image frame. Lastly, super-resolution reconstruction was done through conventional super-resolution reconstruction framework. Experimental results show that the proposed image super-resolution reconstruction method based on SIFT is feasible, and the quality of super-resolution reconstructed images is better than those of classical methods by both subjective evaluation and objective standards.