The resonant retina: exploiting vibration noise to optimally detect edges in an image

We show that, far from being a drawback, the ubiquitous presence of random vibrations in vision systems operating from mobile devices can advantageously be used as a fundamental tool for edge detection. Directly inspired by biology, the concept of dynamic retina uses the random spatiotemporal path, traced by a moving receptor that samples the image over time, as the basis for the edge detection operation. We propose a simple mathematical formalization of the dynamic retina concept that shows that the relevant information needed for edge detection is contained in the modulation of the variance of the output signal delivered by the retina. Based on a sequence of observations, we then use a variance estimator to determine the presence of the image edges. Following again a biological inspiration, more specifically focusing on neuron dynamics, we introduce a threshold type estimator and use its local asymptotic normality to optimize, via the Cramer-Rao relation, the value of the threshold. The optimal threshold value coincides with a maximum of the associated Fisher information and the overall process can therefore be directly interpreted as a stochastic resonance. We end our contribution by reporting some simple experimental illustrations.