Detection of signals in natural images and scenes is limited by both noise
and structure. The purpose of this study is to investigate phenomenological
issues of signal detection in two-component noise. One component had a bro
adband (white) spectrum designed to simulate image noise. The other compone
nt was filtered to simulate two classes of low-pass background structure sp
ectra: Gaussian-filtered noise and power-law noise. Measurements of human a
nd model observer performance are reported for several aperiodic signals an
d both classes of background spectra. Human results are compared with two c
lasses of observer models and are fitted very well by suboptimal prewhiteni
ng matched filter models. The nonprewhitening model with an eye filter does
not agree with human results when background-noise-component power spectru
m bandwidths are less than signal energy bandwidths. (C) 1999 Optical Socie
ty of America [S0740-3232(99)00603-1].