Abstract
In many applications signals are combined in a rather complicated way. Convolved signals are encountered in seismic signal processing, digital speech processing, digital echo removal and digital image restoration. Signals combined in a nonlinear way are encountered in digital signal processing for communication systems and in digital image filtering. Classical linear processing techniques are not so useful in those cases because the superposition property does not hold any more. Therefore, a special class of filters has been developed for the processing of convolved and nonlinearly related signals. They are called homomorphic filters. Their basic characteristic is that they use nonlinearities (mainly the logarithm) to transform convolved or nonlinearly related signals to additive signals and then to process them by linear filters. The output of the linear filter is transformed afterwards by the inverse nonlinearity. Homomorphic filtering has found many applications in digital image processing. It is recognized as one of the oldest nonlinear filtering techniques applied in this area. The main reason for its application is the need to filler multiplicative and signal-dependent noise, whose form was described in chapter 3. Linear filters fail to remove such types of noise effectively. Furthermore, the nonlinearity (logarithm) in the human vision system suggests the use of classical homomorphic filters. Homomorphic filtering can also be used in image enhancement. As we saw in chapter 3, object reflectance and source illumination contribute to the image formation in a multiplicative way. Ideally, the source illumination is constant over the entire image. However, in many practical cases, e.g., in outdoor scenes, source illumination is not constant over the entire scene. Therefore, it can be modeled as noise in the low spatial frequencies. If this noise is removed, the object reflectance is enhanced. Homomorphic filtering has found various practical applications, e.g., in satellite image processing and in the identification of fuzzy fingerprints. Homomorphie filtering has also found several applications in other areas, e.g., in speech processing and in geophysical signal processing. In the following, the theory and several applications of homomorphic filters will be given.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A.V. Oppenheim, R.W. Schafer, Digital signal processing, Prentice-Hall, 1975.
J.M. Tribolet, Seismic applications of homomorphic signal processing, Prentice-Hall, 1979.
W.F. Schreiber, “Image processing for quality improvements”, Proc. of the IEEE, vol. 66, no. 12, pp. 1640–1651, Dec. 1978.
R.W. Fries, J.W. Modestino, “Image enhancement by stochastic homomorphic filtering”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-27, no. 6, pp. 625–637, Dec. 1979.
R.J. Blackwell, W.A. Crisci, “Digital image processing technology and its application to forensic sciences”, Journal of Forensic Sciences, vol. 20, no. 2, pp. 288–304, 1975.
Z.K. Liu, B.R. Hunt, “A new approach to removing cloud cover from satellite imagery”, Computer Vision, Graphics and Image Processing, vol. 25, pp. 252–256, 1984.
O.R. Mitchell, E. Delp, P.L. Chen, “Filtering to remove cloud cover in satellite imagery”, IEEE Transactions on Geoscience Electronics, vol. GE-15, pp. 137–141, 1977.
T. Peli, T.F. Quatieri, “Homomorphie restoration of images degraded by light cloud cover”, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 1984.
J.W. Goodman, “Statistical properties of laser speckle patterns” in Laser speckle and related phenomena, J.C. Dainty editor, Springer Verlag, 1976.
H.H. Arsenault, M. Denis, “Image processing in signal-dependent noise”, Canadian Journal of Physics, vol. 61, pp. 309–317, 1983.
I. Picas, A.N. Venetsanopoulos, “Nonlinear mean filters in image processing”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-34. no. 3, pp. 573–584, June 1986.
I. Pitas, A.N. Venetsanopoulos, “Nonlinear order statistics filters for image filtering and edge detection”, Signal Processing, vol. 10, pp. 395413, 1986.
A. Kundu, S.K. Mitra, P.P. Vaidyanathan, “Application of two-dimensional generalized mean filtering for the removal of impulse noises from images”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-32. no. 3, pp. 600–608, June 1984.
A.V. Oppenheim, R.W. Schafer, T.G. Stockham, “Nonlinear filtering of multiplied and convolved signals”, Proc. of IEEE, vol. 56, pp. 1264–1291, Aug. 1968.
L.R. Rabiner, R.W. Schafer, Digital Processing of Speech Signals, Prentice-Hall, 1978.
J.M. Tribolet, “A new phase unwarping algorithm”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-25, no. 2, pp. 170177, Apr. 1977.
A.K. Jain, Fundamentals of digital image processing, Prentice-Hall, 1989.
J.S. Lim, “Image enhancement”, in Digital Image Processing Techniques, M.P. Ekstrom editor, Academic Press, 1984.
A.K. Jain, C.R. Christensen, “Digital processing of images in speckle noise”, Proc. SPIE, Applications of Speckle Phenomena, vol. 243, pp. 4650, July 1980.
S. Furui, Digital Speech Processing, Synthesis and Recognition, Marcel-Dekker, 1989.
A.V. Oppenheim, R.W. Schafer, Discrete-time signal processing, Prentice-Hall, 1979.
S. Furui, “Cepstral analysis techniques for automatic speaker verification”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-29, no. 2, pp. 254–272, Apr. 1981.
S.B. Davis, P. Mermelstein, “Comparison of parametric representations of monosyllabic word recognition in continuously spoken sentence”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-28, no. 4, pp. 357–366, Apr. 1980.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1990 Springer Science+Business Media New York
About this chapter
Cite this chapter
Pitas, I., Venetsanopoulos, A.N. (1990). Homomorphic Filters. In: Nonlinear Digital Filters. The Springer International Series in Engineering and Computer Science, vol 84. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6017-0_7
Download citation
DOI: https://doi.org/10.1007/978-1-4757-6017-0_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5120-5
Online ISBN: 978-1-4757-6017-0
eBook Packages: Springer Book Archive