Two-dimensional Teager filters

Publisher Summary This chapter presents a framework that describes a category of homogeneous quadratic Volterra filters to which the Teager filter belongs. The Teager filter is a homogeneous quadratic Volterra filter. The chapter analyzes their properties and derives a two-dimensional version of the Teager filter, stating that the Teager filter has the property that sinusoidal inputs generate constant outputs that are approximately proportional to the square of the input frequency. The chapter presents an intuitive interpretation of the frequency response of these filters, which facilitates a better understanding of their properties. This filter and modifications of it have been used successfully in image enhancement applications. Two-dimensional Teager filters have properties that are desirable for image enhancement since they can be approximated as mean-weighted highpass filters. Finally, the chapter illustrates some examples of the application of image enhancement, image interpolation, and image halftoning.

[1]  H. M. Teager,et al.  Evidence for Nonlinear Sound Production Mechanisms in the Vocal Tract , 1990 .

[2]  Giovanni Ramponi,et al.  Enhancing document images with a quadratic filter , 1993, Signal Process..

[3]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[4]  G. Ramponi,et al.  A computational method for the design of 2-D nonlinear Volterra filters , 1988 .

[5]  L. Chua,et al.  Frequency domain analysis of nonlinear systems: general theory , 1979 .

[6]  Sanjit K. Mitra,et al.  A new class of nonlinear filters for image enhancement , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[7]  James F. Kaiser,et al.  Some useful properties of Teager's energy operators , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  J. F. Kaiser,et al.  On a simple algorithm to calculate the 'energy' of a signal , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[9]  G. Ramponi.  Edge extraction by a class of second-order nonlinear filters , 1986 .

[10]  H. Teager.  Some observations on oral air flow during phonation , 1980 .

[11]  B. E. Rogowitz.  The human visual system: a guide for the display technologist , 1983 .

[12]  W. Rugh.  Nonlinear System Theory: The Volterra / Wiener Approach , 1981 .

[13]  Jerry M. Mendel,et al.  Lessons in digital estimation theory , 1986 .

[14]  S. A. Billings,et al.  Spectral analysis for non-linear systems, Part I: Parametric non-linear spectral analysis , 1989 .

[15]  Sanjit K. Mitra,et al.  A general framework for quadratic Volterra filters for edge enhancement , 1996, IEEE Trans. Image Process..

[16]  L. Kaufman,et al.  Handbook of perception and human performance , 1986 .

[17]  Bernard C. Picinbono.  Quadratic filters , 1982, ICASSP.

[18]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[19]  Stephen A. Billings,et al.  Spectral analysis for non-linear systems, Part II: Interpretation of non-linear frequency response functions , 1989 .

[20]  Sanjit K. Mitra,et al.  Image Representation Using Block Pattern Models and Its Image Processing Applications , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Robert B. Dunn,et al.  Detection of transient signals using the energy operator , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[22]  Melvin L. Rubin,et al.  Fundamentals of visual science , 1970 .

[23]  G. Sicuranza.  Quadratic filters for signal processing , 1992, Proc. IEEE.

[24]  Sanjit K. Mitra,et al.  Detail‐enhanced error diffusion , 1996 .

[25]  M. Schetzen.  The Volterra and Wiener Theories of Nonlinear Systems , 1980 .

[26]  Ioannis Pitas,et al.  Nonlinear Digital Filters - Principles and Applications , 1990, The Springer International Series in Engineering and Computer Science.

[27]  Sanjit K. Mitra,et al.  EDGE-ENHANCED IMAGE ZOOMING , 1996 .