Abstract

A number of task-specific approaches to the assessment of image quality are treated. Both estimation and classification tasks are considered, but only linear estimators or classifiers are permitted. Performance on these tasks is limited by both quantum noise and object variability, and the effects of postprocessing or image-reconstruction algorithms are explicitly included. The results are expressed as signal-to-noise ratios (SNR’s). The interrelationships among these SNR’s are considered, and an SNR for a classification task is expressed as the SNR for a related estimation task times four factors. These factors show the effects of signal size and contrast, conspicuity of the signal, bias in the estimation task, and noise correlation. Ways of choosing and calculating appropriate SNR’s for system evaluation and optimization are also discussed.

© 1990 Optical Society of America

Objective assessment of image quality. IV. Application to adaptive optics

Harrison H. Barrett, Kyle J. Myers, Nicholas Devaney, and Christopher Dainty
J. Opt. Soc. Am. A 23(12) 3080-3105 (2006)

Objective assessment of image quality. II. Fisher information, Fourier crosstalk, and figures of merit for task performance

Harrison H. Barrett, J. L. Denny, Robert F. Wagner, and Kyle J. Myers
J. Opt. Soc. Am. A 12(5) 834-852 (1995)

Aperture optimization for emission imaging:effect of a spatially varying background

K. J. Myers, J. P. Rolland, H. H. Barrett, and R. F. Wagner
J. Opt. Soc. Am. A 7(7) 1279-1293 (1990)

Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability

Craig K. Abbey and Harrison H. Barrett
J. Opt. Soc. Am. A 18(3) 473-488 (2001)

Objective assessment of image quality. V. Photon-counting detectors and list-mode data

Luca Caucci and Harrison H. Barrett
J. Opt. Soc. Am. A 29(6) 1003-1016 (2012)