title: Generalizing the nonlocal-means to super-resolution reconstruction.
abstract: 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 nonglobal 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 paper, we base our solution on the Nonlocal-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.
authors.affiliation: Department of Computer Science, The Technion-Israel Institute of Technology, Haifa, Israel. matanpr@cs.technion.ac.il
authors.forename: Matan
authors.initials: M
authors.lastname: Protter
authors.forename: Michael
authors.initials: M
authors.lastname: Elad
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Takeda
authors.forename: Peyman
authors.initials: P
authors.lastname: Milanfar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity