Experimental determination of object statistics from noisy images

Matthew A. Kupinski, Eric Clarkson, John W. Hoppin, Liying Chen, Harrison H. Barrett

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Modern imaging systems rely on complicated hardware and sophisticated image-processing methods to produce images. Owing to this complexity in the imaging chain, there are numerous variables in both the hardware and the software that need to be determined. We advocate a task-based approach to measuring and optimizing image quality in which one analyzes the ability of an observer to perform a task. Ideally, a task-based measure of image quality would account for all sources of variation in the imaging system, including object variability. Often, researchers ignore object variability even though it is known to have a large effect on task performance. The more accurate the statistical description of the objects, the more believable the task-based results will be. We have developed methods to fit statistical models of objects, using only noisy image data and a well-characterized imaging system. The results of these techniques could eventually be used to optimize both the hardware and the software components of imaging systems.

Original languageEnglish (US)
Pages (from-to)421-429
Number of pages9
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number3
StatePublished - Mar 2003

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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