Statistical performance analysis of super-resolution.
Recently, there has been a great deal of work developing super-resolution algorithms for combining a set of low-quality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the joint task of registering and fusing the low-quality image data. While many such algorithms have been proposed, very little work has addressed the performance bounds for such problems. In this paper, we analyze the performance limits from statistical first principles using Cramér-Rao inequalities. Such analysis offers insight into the fundamental super-resolution performance bottlenecks as they relate to the subproblems of image registration, reconstruction, and image restoration.
Robinson
Dirk
D
Department of Electrical Engineering, University of California at Santa Cruz, CA 95064, USA. dirkr@ee.ucsc.edu
Milanfar
Peyman
P
D000465:Algorithms; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique