P.K. Varshney; "Stochastic Resonance in Signal/Image...", 17.4., 13:40
  • FENS
  • P.K. Varshney; "Stochastic Resonance in Signal/Image...", 17.4., 13:40

You are here

Faculty of Engineering and Natural Sciences






 Stochastic Resonance in Signal/Image Processing: Can Addition of Noise Improve Performance?


Pramod K. Varshney
Distinguished Professor
Department of Electrical Engineering and Computer Science
Syracuse University



Stochastic resonance (SR) is a phenomenon in which the performance of some nonlinear systems can be enhanced by adding suitable noise under certain conditions. This counter-intuitive phenomenon has been observed in many fields such as Physics, Biology, and Neuroscience. The basic idea of performance enhancement by adding noise has been practiced in signal processing for some time, e.g., dithering in digital audio systems. We have recently formalized this idea and have developed a theoretical framework to analyze the SR effect in signal detection and estimation systems. We have obtained fundamental results on whether or not a system is improvable by SR and if yes, what the optimum noise probability density function is for the specific signal processing task. The results are quite surprising in that the form of the optimal noise is quite simple. This talk will introduce the phenomenon of SR, present our recent results and will conclude with application of SR to a variety of applications such as signal detection, parameter estimation and image processing tasks such as

medical image enhancement and image denoising.

Pramod K. Varshney received the B.S. degree in electrical engineering and computer science (with highest honors), and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign in 1972, 1974, and 1976 respectively. Since 1976 he has been with Syracuse University, Syracuse, NY where he is currently a Distinguished Professor of Electrical Engineering and Computer Science and the Research Director of the New York State Center for Advanced Technology in Computer Applications and Software Engineering. His current research interests are in distributed sensor networks and data fusion, detection and estimation theory, wireless communications, image processing, and remote sensing. He is an IEEE Fellow and has received numerous awards. He serves as a distinguished lecturer for the AES society of the IEEE. He was the President of International

Society of Information Fusion during 2001.

Thursday, April 17, 2008, 13:40, FENS G032