6533b85afe1ef96bd12b8bd8
RESEARCH PRODUCT
Statistical Performance Analysis of a Fast Super-Resolution Technique Using Noisy Translations.
Pierre ChainaisAymeric Leraysubject
FOS: Computer and information sciences[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingPositioning systemComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONsuper-resolution02 engineering and technologyIterative reconstructionMethodology (stat.ME)[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingPosition (vector)[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringComputer visionImage resolutionStatistics - Methodologyerror analysis[STAT.AP]Statistics [stat]/Applications [stat.AP]business.industryreconstruction algorithms[ STAT.AP ] Statistics [stat]/Applications [stat.AP]Process (computing)high-resolution imaging020206 networking & telecommunicationsFunction (mathematics)Computer Graphics and Computer-Aided DesignSuperresolutionperformance evaluation[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]microscopy020201 artificial intelligence & image processingAlgorithm designArtificial intelligencebusinessSoftwaredescription
It is well known that the registration process is a key step for super-resolution reconstruction. In this work, we propose to use a piezoelectric system that is easily adaptable on all microscopes and telescopes for controlling accurately their motion (down to nanometers) and therefore acquiring multiple images of the same scene at different controlled positions. Then a fast super-resolution algorithm \cite{eh01} can be used for efficient super-resolution reconstruction. In this case, the optimal use of $r^2$ images for a resolution enhancement factor $r$ is generally not enough to obtain satisfying results due to the random inaccuracy of the positioning system. Thus we propose to take several images around each reference position. We study the error produced by the super-resolution algorithm due to spatial uncertainty as a function of the number of images per position. We obtain a lower bound on the number of images that is necessary to ensure a given error upper bound with probability higher than some desired confidence level.
year | journal | country | edition | language |
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2014-11-03 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |