Displaying Confidence Images

Abstract

Algorithms for computing images result in an estimate of an image. The image may result from deblurring a measured image, from deconvolving a set of measurements, or from computing an image by modeling physical processes such as the weather. These computations provide an estimated value for each pixel in the image. What is lacking, however, is an estimate of the statistical confidence that we can have in those pixel values or in the features they form. In this work we discuss novel ways to display confidence information, using an algorithm called {\sf Twinkle}, in order to give the viewer valuable visual insight into uncertainties. The technique is useful whether the confidence information is in the form of a confidence interval or a distribution of possible values. We demonstrate how to display confidence information in a variety of applications: weather forecasts, intensity of a star, and rating a potential tumor in a diagnostic image. (Also cross-referenced as UMIACS-TR-2000-51)