Nonlinear self-filtering of noisy images via dynamical stochastic resonance


From night vision and objects overwhelmed by sunlight to jammed signals and those that are purposely encrypted, detecting low-level or hidden signals is a fundamental problem in imaging. Here, we develop and exploit a new type of stochastic resonance, in which nonlinear coupling allows signals to grow at the expense of noise, to recover noise-hidden images propagating in a self-focusing medium. The growth rate is derived analytically by treating the signal–noise interaction as a photonic beam–plasma instability and matches experimentally measured resonances in coupling strength, noise statistics and modal content of the signal. This is the first observation of nonlinear intensity exchange between coherent and spatially incoherent light and the first demonstration of spatial coherence resonance for a dynamically evolving signal. The results suggest a general method of reconstructing images through seeded instability and confirm information limits predicted, but not yet observed, in nonlinear communications systems.

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Figure 1: Experimental set-up.
Figure 2: Nonlinear self-filtering as a function of signal–noise coupling.
Figure 3: Nonlinear self-filtering as a function of noise.
Figure 4: Measurement versus theory of dynamical stochastic resonance.


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The authors would like to thank S. Verdú, E.E. Narimanov and P.R. Prucnal for valuable discussions. This work was supported by the National Science Foundation, the Department of Energy, and the Air Force Office of Scientific Research.

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Correspondence to Jason W. Fleischer.

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Dylov, D., Fleischer, J. Nonlinear self-filtering of noisy images via dynamical stochastic resonance. Nature Photon 4, 323–328 (2010).

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