Abstract
There are many ways in which the human visual system works to reduce the inherent redundancy of the visual information in natural scenes, coding it in an efficient way. The non-linear response curves of photoreceptors and the spatial organization of the receptive fields of visual neurons both work toward this goal of efficient coding. A related, very important aspect is that of the existence of post-retinal mechanisms for contrast enhancement that compensate for the blurring produced in early stages of the visual process. And alongside mechanisms for coding and wiring efficiency, there is neural activity in the human visual cortex that correlates with the perceptual phenomenon of lightness induction. In this paper we propose a neural model that is derived from an image processing technique for histogram equalization, and that is able to deal with all the aspects just mentioned: this new model is able to predict lightness induction phenomena, and improves the efficiency of the representation by flattening both the histogram and the power spectrum of the image signal.
Highlights
The human visual system works in many ways in order to efficiently encode the visual information coming from natural environments, reducing its inherent redundancy, as proposed in the seminal work of Barlow (1961)
In this paper we propose a neural model that is derived from an image processing technique for histogram equalization, and that is able to deal with all the aspects just mentioned: this new model is able to predict lightness induction phenomena, and improves the efficiency of the representation by flattening both the histogram and the power spectrum of the image signal
The visual system is able to achieve a visual acuity beyond the limit imposed by the number of photoreceptors at the retina: in their classical paper on contrast constancy, Georgeson and Sullivan (1975) suggest that there are cortical mechanisms for contrast enhancement that compensate for the blurring produced in early stages of the visual process
Summary
The human visual system works in many ways in order to efficiently encode the visual information coming from natural environments, reducing its inherent redundancy, as proposed in the seminal work of Barlow (1961) (see Olshausen and Field, 2000 for a review). The term lightness induction or achromatic induction designates the visual phenomenon by which the perceived reflectance of an object depends on its surround. It can take the form of lightness contrast, when the object’s lightness shifts away from that of its surroundings: a dark object on a light background appears even darker, or a light object in a dark surround becomes even lighter. Our contribution in this paper is to propose a neural activity model, a partial differential equation (PDE) in the form of a Wilson-Cowan equation (Wilson and Cowan, 1972), which takes care simultaneously of the four aspects mentioned above: it performs histogram equalization, spectrum whitening, contrast enhancement, and it predicts lightness induction. The proposed model is based on a state of the art method for color and contrast enhancement from the image processing literature, so we start the following section reviewing some key image processing concepts
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