Image reconstruction using bistable gradient neural-like networks

We present a novel bistable gradient neural-like network (BGN) and demonstrate its potential applications for image processing where data are corrupted by high intensity additive or multiplicative noise, or both. The goal is to develop an adaptive computational network that incorporates the physics of many-body interactions and operational rules of biological systems. We apply BGN to restore unknown images, which have been degraded by random blurring matrix and additive Gaussian white noise. The competitive advantage of BGN derives from its nice generalization capabilities, existence of the unique attractor with the lowest energy that is worked out when several patterns are stored by the network, and fast guaranteed convergence to this attractor