Visual signal detection with two-component noise: low-pass spectrum effects

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 broadband (white) spectrum designed to simulate image noise. The other component was filtered to simulate two classes of low-pass background structure spectra: Gaussian-filtered noise and power-law noise. Measurements of human and model observer performance are reported for several aperiodic signals and both classes of background spectra. Human results are compared with two classes of observer models and are fitted very well by suboptimal prewhitening matched filter models. The nonprewhitening model with an eye filter does not agree with human results when background-noise-component power spectrum bandwidths are less than signal energy bandwidths.