Computer simulations of an extended version of a neural model of lightness perception [1,2] are presented. The model provides a unitary account of several key aspects of spatial lightness phenomenology, including contrast and assimilation, and asymmetries in the strengths of lightness and darkness induction. It does this by invoking mechanisms that have also been shown to account for the overall magnitude of dynamic range compression in experiments involving lightness matches made to real-world surfaces [2]. The model assumptions are derived partly from parametric measurements of visual responses of ON and OFF cells responses in the lateral geniculate nucleus of the macaque monkey [3,4] and partly from human quantitative psychophysical measurements. The model’s computations and architecture are consistent with the properties of human visual neurophysiology as they are currently understood. The neural model's predictions and behavior are contrasted though the simulations with those of other lightness models, including Retinex theory [5] and the lightness filling-in models of Grossberg and his colleagues [6].
The Random spray Retinex (RSR) algorithm was developed by taking into consideration the mathematical description of Milano-Retinex. The RSR substituted random paths with random sprays. Mimicking some characteristics of the human visual system (HVS), this article proposes two variants of RSR adding a mechanism of region of interest (ROI). In the first proposed model, a cone distribution based on anatomical data is considered as ROI. In the second model, the visual resolution depending on the visual field based on the knowledge of visual information processing is considered as ROI. We have measured actual eye movements using an eye-tracking system. By using the eye-tracking data, we have simulated the HVS using test images. Results show an interesting qualitative computation of the appearance of the processed area around real gaze points.