From image processing to computational neuroscience: a neural model based on histogram equalization

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.