Nonlinear analog networks for image smoothing and segmentation

Stochastic artificial retinas: algorithm, optoelectronic circuits, and implementation

An analogy can be established between image processing and statistical mechanics. Many early- and intermediate-vision tasks such as restoration, image segmentation, and motion detection can be formulated as optimization problems that consist in finding the ground states of an energy function. This approach yields excellent results, but, once it is implemented in conventional sequential workstations, the computational loads are too extensive for practical purposes, and even fast suboptimal optimization approaches are not sufficient. We elaborate on dedicated massively-parallel integrated circuits, called stochastic artificial retinas, that minimize the energy function at a video rate. We consider several components of these artificial retinas: stochastic algorithms for restoration tasks in the presence of discontinuities, dedicated optoelectronic hardware to implement thermal motion by photodetection of speckles, and hybrid architectures that combine optoelectronic, asynchronous-analog, and clocked-digital circuits.