Registration of Under-Sampled Images via Higher Resolution Spectrum Restoration

High accuracy image registration is critical for the success of multi-frame super-resolution. Conventionally, the shift between images are estimated directly based on the undersampled low-resolution (LR) image data. However, the high-frequency of LR data is unreliable due to the aliasing effect of sub-sampling, which will deteriorate the accuracy of registration. This paper proposes to resolve the aliasing by converting the LR images to high-resolution (HR) domain and then perform registration on the restored HR spectrum. To recover the HR spectrum for each corresponding LR image, we fuse the LR images into one HR image and project the estimation difference back to the reconstructed HR spectrum iteratively. To address the unequal reliabilities of different restored frequencies, weighted least square is employed to improve the precision of registration. Experimental results show that the proposed method can outperform other existing methods and improve the quality of super-resolution image.National Basic Research Program of China (973 Program) [2015CB351800]; National Natural Science Foundation of China [61370114, 61421062]; Beijing Natural Science Foundation [4132039]; Research Fund for the Doctoral Program of Higher Education [20120001110090]; Cooperative Medianet Innovation CenterCPCI-S(ISTP