Linear image processing algorithms have received considerable attention during the last several decades. The basic hypotheses for the development of linear models and linear signal processing algorithms are stationarity and Gaussianity. To achieve improved performance, algorithms must take into account: (1) nonlinear effects in the human visual system and (2) nonlinear behavior of the image acquisition systems. The hypotheses of stationarity and Gaussianity do not hold in the case of image signals. This has led to a growing interest in the development of nonlinear image processing methods in recent years. The Volterra filter is a special case of the polynomial filters. It is based upon an input-output relation expressed in the form of a truncated Volterra series. Simplest types are the quadratic filters corresponding to the first nonlinear term in the Volterra expansion. In this talk, we present next four specific image processing applications of quadratic Volterra filters.
Sanjit K. Mitra is a Research Professor in the Department of Electrical & Computer Engineering, University of California, Santa Barbara and Professor Emeritus, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles. He has held visiting appointments in Australia, Austria, Brazil, Croatia, Finland, Germany, India, Japan, Norway, Singapore, Turkey, and the United Kingdom. Dr. Mitra has published over 700 papers in the areas of analog and digital signal processing, and image processing. He has also authored and co-authored twelve books, and holds five patents. He has presented 29 keynote and/or plenary lectures at conferences held in the United States and 16 countries abroad. Dr. Mitra has served IEEE in various capacities including service as the President of the IEEE Circuits & Systems Society in 1986.