Optimizing image quality using test signals: Trading off blur, noise and contrast

Abstract

Objective image quality assessment (QA) is crucial in order to improve imaging systems and image processing techniques. In medical imaging, model observers that estimate signal detectability, have become widespread and promising as a means to avoid costly human observer experiments. However, signal detectability alone does not give the complete picture: one may also be interested in optimizing several independent quality factors (e.g. contrast, spatial resolution, noise). In recent work, we have proposed the channelized joint observer (CJO), to jointly detect and estimate random parametric signals in images, a so-called signal-known-statistically (SKS) detection task. In this paper, we show how the estimation capabilities of the CJO can be exploited to estimate several image quality factors in degraded images, through signal insertion. By fixing the signal detectability, we illustrate how to benefit from the trade-offs that exist between the different quality factors. Our method is in the first place intended to aid medical image reconstruction techniques and medical display design, although the technique can also be useful in a much wider context.