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Approaching a unified model of pattern detection and brightness perception

In order to develop a human vision model to simulate both grating detection and brightness perception, we have chosen four visual functional components. They include a front-end low-pass filter, a cone-type dependent 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. The model outputs were fitted to contrast sensitivity functions over 7 mean illuminance levels ranging from 0.0009 to 900 trolands simultaneously with a set of 6 free parameters. The fits account for 97.8% of the total variance in the reported experimental data. Furthermore, the same model was used to simulate contrast and brightness perception. Some visual patterns that can produce simultaneous contrast or crispening effect were used as input images to the model. The outputs are consistent with the perceived brightness, using the same set of parameter values that was used in the above-mentioned fits. The model also simulated the perceived contrast contours on seeing a frequency-modulated grating and the whiteness percepts at different adaptation levels. In conclusion, a model that is based on simple visual properties is promising for deriving a unified model of pattern detection and brightness perception.