Abstract
We consider 8bit grey scale images as two-dimensional input signals. After the addition of stochastic patterns the resultant grey value matrix is two-state filtered with a global threshold. Depending on the threshold value non-dynamical stochastic resonance (SR) or suprathreshold stochastic resonance (SSR) conditions can be set up. Both phenomena are quantified appropriately through the transin- formation and illustrated by application to a sample image. Key issue of our in- vestigation will be the idea that the signal reconstruction is enhanced by not using spatially uncorrelated noise patterns but stochastic patterns which reflect features of the input. We exemplify this notion by employing stochastic Swift-Hohenberg patterns for both the input signal and the additional noise fields.
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Preund, J.A. (2000). Stochastic Resonance with Images and Spatially Correlated Stochastic Patterns. In: Freund, J.A., Pöschel, T. (eds) Stochastic Processes in Physics, Chemistry, and Biology. Lecture Notes in Physics, vol 557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45396-2_16
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DOI: https://doi.org/10.1007/3-540-45396-2_16
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