Super-Resolution-Reconstruction of Image Sequences Without Explicit Motion Estimation
July 8th, 2008
SIAM Imaging Science 2008, San-Diego. Special Session on Locally Adaptive Patch-based Image and Video Restoration - Part II.

Super-resolution reconstruction proposes a fusion of several low quality images into one higher quality result with better optical resolution. Classic super resolution techniques strongly rely on the availability of accurate motion estimation for this fusion task. When the motion is estimated inaccurately, as often happens for non-global motion fields, annoying artifacts appear in the super-resolved outcome. Encouraged by recent developments on the video denoising problem, where state-of-the-art algorithms are formed with no explicit motion estimation, we seek a super-resolution algorithm of similar nature that will allow processing sequences with general motion patterns. In this talk we base our solution on the Non-Local-Means (NLM) algorithm. We show how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation. Results on several test movies show that the proposed method is very successful in providing super-resolution on general sequences.

Joint work with Matan Protter (CS - Technion), Hiro Takeda and Peyman Milanfar (UCSC).