Simulating Visual Pattern Detection and Brightness Perception Based on Implicit Masking

A quantitative model of implicit masking, with a front-end low-pass filter, a retinal local compressive nonlinearity described by a modified Naka-Rushton equation, a cortical representation of the image in the Fourier domain, and a frequency-dependent compressive nonlinearity, was developed to simulate visual image processing. The model algorithm was used to estimate contrast sensitivity functions over 7 mean illuminance levels ranging from 0.0009 to 900 trolands, and fit to the contrast thresholds of 43 spatial patterns in the Modelfest study. The RMS errors between model estimations and experimental data in the literature were about 0.1 log unit. In addition, the same model was used to simulate the effects of simultaneous contrast, assimilation, and crispening. The model results matched the visual percepts qualitatively, showing the value of integrating the three diverse perceptual phenomena under a common theoretical framework.