OPPENHEIM A., SCHAFER R., STOCKHAM T.: Nonlinear filtering of multiplied and convolved signals. Proc. of the IEEE 56, 8 (1968), 1264-1291.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... Uniform Spatially Varying Time independent Time dependent Tumblin Rushmeier 1993 [41] Upstill 1985 [43] Miller et al. 1984 [22] Cohen et al. 2001 [6] Tumblin et al. 1999 [40] Scheel et al. 2000 [33] Ward Larson et al. 1997 [18] Ward 1994 [45] Oppenheim et al. 1968 [26] Stockham 1993 [38] Chiu et al. 1993 [5] Schlick 1994 [34] Jobson et al. 1997 [15] Pattanaik et al. 1998 [27] Tumblin Turk 1999 [42] Ashikhmin 2002 [2] Fattal et al. 2002 [9] Reinhard et al. 2002 [31] Durand Dorsey 2002 [8] Ferwerda et al. 1996 [10] Durand Dorsey 2000[7] ....

....one representing the high intensities and the other the low intensities. During display, these two texture maps are recombined with the aid of a dynamically adjustable exposure level to guide the overall intensity of the result. 3. 2 Spatially varying operators Oppenheim, Schafer and Stockham s [26] work on non linear filtering in 1968 appears to be the earliest attempt at tone reproduction in computer graphics. They describe the problem of excessive dynamic range and suggest a method for simultaneously reducing dynamic range and enhancing contrast using homorphic filtering. An image can be ....

A. Oppenheim, R. Schafer, and T. Stockham. Nonlinear filtering of multiplied and convolved signals. In Proceedings of the IEEE, volume 56, pages 1264--1291, August 1968. 12

....filtering of the time sequences of spectral parameters (the spectral trajectories) of the input signal in a domain in which the speech and interference are (approximately) additive can suppress the e#ects of the interference. This approach is essentially an extension of homomorphic filtering [OSTGS68] Thus, it is possible to suppress additive noise whose spectrum changes more slowly or more rapidly than do the portions of the speech signal that carry linguistic information by filtering power spectral trajectories [HMR91] because the speech and noise signals are additive in the power ....

Alan V. Oppenheim, Ronald W. Schafer, and Jr. Thomas G. Stockham. Nonlinear filtering of multiplied and convolved signals. Proceedings of the IEEE, 56(8):1264--1291, August 1968.

....where represents convolution, d and f are the unknown PSF and original image, and n is the noise signal. g is the blurred and noisy image which is available for deblurring. The problem of identifying the original signal without a priori knowledge of the PSF has been termed blind deconvolution [62], blind deblurring [63] and blur identification [64] The problem will generally be referred to as blur identification here. The complexity of the blur identification problem suggests that the techniques 86 be limited to PSF s that are shift invariant. The problem becomes much more difficult ....

....be crucial [66] In (7.6) however, a different problem exists. As discussed in [68] the phase equation suffers from ambiguities in the inversion process. Thus, the linearity required for (7.6) does not hold unless the phases are treated in an unusual way. Although techniques exist for doing this [62], Stockham et al. were not successful in applying such techniques to the blur identification problem. However, a number of developments have been made in the area of phase from magnitude in Fourier transforms [69 74] since Stockham et al. conceived of their technique. Such developments would more ....

A. V. Oppenheim, R. W. Schafer, and T. G. Stockham, Jr., "Nonlinear filtering of multiplied and convolved signals," Proceedings of the IEEE, vol. 56, pp. 1264--1291, August 1968.

....observations too. One of well known multichannel models is so called convolutional model [Ziolkowski 1984] having been widely used in echo cancellation [Oppenheim et al. 1989] speech analysis, and seismic exploration [Hsueh et al. 1985] Its application relies on homomorphic deconvolution [Oppenheim et al. 1968] that treats signal superimpositions as a result of a basic waveform convolution by a pulse train. Thus, such a model introduces identical channels, which, unfortunately, restricts its relatively simple blind deconvolution solution to a smaller class of problems. However, extensions of the ....

....blind homomorphic deconvolution [Zazula et al. 1994 (1) Starting from the MISO model in Fig. 1.a and Eq. 1, the EMG signal y(n) may be partitioned into subsequent intervals obtained via a sliding rectangular, or other type, window. These intervals are further used in a cepstrum calculation [Oppenheim et al. 1968, Oppenheim et al. 1989] In every interval, some of the signal components y i (n) are present. We found out that a linear filtering in the cepstral domain can extract only one of these components if A 2 l (L Gamma 1) L X i=1;i6=l A 2 i ; where A l is the amplitude of the extracted ....

Oppenheim A. V., Schafer R. W., Stockham T. G. (1968) Nonlinear Filtering of Multiplied and Convolved Signals. Proceedings of the IEEE, 56: 1264-1291.

....filters: 1. Homomorphic filter: By taking the logarithm of x = sn, we obtain ln x = ln s ln n (16) thus transforming the problem to one of additive noise filtering. By averaging the logarithmized image the additive noise is suppressed and by exponentiating the result an estimate of s is obtained [21]. 2. Frost filter: Following [10] Frost et al. 4, 3] derived a MMSE Wiener filter, on the basis of a first order autoregressive model for the image signal, of the form h(m; n) K 1 ffe Gammaffk(m;n) Gamma(m 0 ;n 0 )k (17) where (m 0 ; n 0 ) are the coordinates of the central pixel in the ....

A. V. Oppenheim, R. W. Schafer, and T. G. Stockham, Jr., "Nonlinear Filtering of Multiplied and Convolved Signals," Proc. IEEE, vol. 56, Aug. 1968, pp. 1264--1291.

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OPPENHEIM A., SCHAFER R., STOCKHAM T.: Nonlinear filtering of multiplied and convolved signals. Proc. of the IEEE 56, 8 (1968), 1264-1291.

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Oppenheim, A.V., R.W. Schafer, and T.G. Stockham, Jr., Non-Linear Filtering of Multiplied and Convolved Signals. Proc. IEEE, 1968. 56(8): p. 1264-1291.

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A. V.. Oppenheim, R. N. Schafer, and T. G. Stockham, "Nonlinear filtering of multiplied and convolved signals," Proc. IEEE 56, 1264-1291 (1968).

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A. Oppenheim, R. Schafer, and T. Stockham, "Nonlinear filtering of multiplied and convolved signals", Proc. of the IEEE 56(2), pp. 1264--1291, 1968.

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A. Oppenheim, R. Schaffer, and T. Stockham, "Nonlinear filtering of multiplied and convolved signals," Proceedings of the IEEE 56(2), pp. 1264--1291, 1968.

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: A. V. Oppenheim, R. W. Schafer, and T. G. Stockham, "Nonlinear filtering of multiplied and convolved signals," Proc. IEEE, vol. 56, no. 8, 1968, 1264-91.

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