Multiframe Super-Resolution Reconstruction Based on Cycle-Spinning

Abstract

A multiframe super-resolution (SR) reconstruction algorithm based on cycle-spinning (CS) is proposed. We utilize the relative motion information of sequential images to construct a CS-based framework for the resolution enhancement. The unique feature of the proposed algorithm is that it is effective for low-resolution (LR) images with various point spread function (PSF) and noise characteristics, even if the degradation models are unknown for the imaging system. Moreover, the computational complexity is inexpensive. Experiments demonstrate the effectiveness of the proposed method and show the superiority to previous methods in objective and subjective qualities.