Abstract

Reconstruction-based super-resolution from motion video has been an active area of study in computer vision and video analy- sis. Image alignment is a key component of super-resolution algorithms. Almost all previous super-resolution algorithms have assumed that stan- dard methods of image alignment can provide accurate enough alignment for creating super-resolution images. However, a systematic study of the demands on accuracy of multi-image alignment and its efiects on super- resolution has been lacking. Furthermore, implicitly or explicitly most algorithms have assumed that the multiple video frames or specific re- gions of interest are related through global parametric transformations. From previous works, it is not at all clear how super-resolution performs under alignment with piecewise parametric or local optical flow based methods. This paper is an attempt at understanding the infiuence of image alignment and warping errors on super-resolution. Requirements on the consistency of optical flow across multiple images are studied and it is shown that errors resulting from traditional flow algorithms may render super-resolution infeasible.