Abstract
The need for extrapolation of signals in time domain or frequency domain often arises in many applications in the area of signal and image processing. One of the approaches used for the extrapolation of the signals is the method of alternating projections (MAP) in conventional Fourier domains (CFD). Here we propose an extension of this approach using the fractional Fourier transform (FRFT) called here as the method of alternating projections in the FRFT domains (MAPFD). It is shown through the simulation results that the mean square error (MSE) between the true signal and the extrapolated signal obtained from the given signal is a function of the angle parameter of the FRFT, and the MAPFD gives lower MSE than the MAP in the CFD for the class of signals bandlimited in the FRFT domains, e.g., chirp signals. Moreover, the performance of the extrapolation using the MAPFD is shown to be shift-variant along the time axis.
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Abbreviations
- α :
-
FRFT angle parameter
- F α (u):
-
FRFT of the signal f (t)
- K α (t,u):
-
The kernel of the FRFT
- \(\varsigma\) :
-
The time-limiting operation
- \({\Im}\) :
-
The identity operator
- \({\wp}\) :
-
The bandlimiting operator in the CFD
- \({\wp}_{\alpha}\) :
-
The bandlimiting operator in the FRFT domain with angle \(\alpha \ne \pi \mathord{\left/ {\vphantom {\pi 2}} \right. \kern-\nulldelimiterspace} 2\)
- Ω α :
-
Bandwidth of the signal f (t) in the FRFT domain α
- rect(t/T):
-
Rectangular window of width T around origin of the time axis
- g n (t):
-
The extrapolated signal after nth iteration
- f e (t):
-
The error signal between the extrapolated signal g n (t) and the true signal f (t)
References
Jain A.K. (2002). Fundamentals of Digital Image processing. Prentice Hall of India, New Delhi
Castleman K.R. (1996). Digital Image Processing. Prentice Hall, Englewood Cliffs
Cetin A.E., Ozaktas H. and Ozaktas H.M. (2003). Resolution enhancement of low resolution wavefields with POCS algorithm. Elect. Lett. 39(25): 1808–1810
Rajan D. and Choudhary S. (2001). Generalized interpolation and its application in super-resolution imaging. Image Vis. Comput. 19: 957–969
Marks II R.J. (1993). Advanced Topics in Shannon Sampling and Interpolation Theory. Springer, New York
Xia X.-G. (1996). On bandlimited signals with fractional Fourier transform. IEEE Signal Proc. Lett. 3(3): 72–74
Ozaktas H.M., Zalevsky Z. and Kutay M.A. (2001). The Fractional Fourier Transform with Applications in Optics and Signal Processing. Wiley, Chichester
Pei S.-C., Yeh M.-H. and Luo T.-L. (1999). Fractional Fourier series expansion for finite signals and dual extension to discrete-time fractional Fourier transform. IEEE Trans. Signal Proc. 47(10): 2883–2888
Namias V. (1980). The fractional Fourier transform and its application to quantum mechanics. J. Inst. Math Appl. 25: 241–265
Almeida L.B. (1994). The fractional Fourier transform and time-frequency representations. IEEE Trans. Signal Proc. 42: 3084–3091
Zayed A.I. (1998). A convolution and product theorem for the fractional Fourier transform. IEEE Signal Proc. Lett. 5(4): 101–103
Ozaktas H.M. and Barshan B. (1994). Convolution, filtering and multiplexing in fractional Fourier domains and their relationship to chirp and wavelet transforms. J. Opt. Soc. Am. A 11: 547–559
Lee S.Y. and Szu H.H. (1994). Fractional Fourier transforms, wavelet transform and adaptive neural network. Opt. Eng. 33: 2326–2330
Almeida L.B. (1997). Product and convolution theorems for the fractional Fourier transform. IEEE Signal Proc. Lett. 4(1): 15–17
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Sharma, K.K., Joshi, S.D. Extrapolation of signals using the method of alternating projections in fractional Fourier domains. SIViP 2, 177–182 (2008). https://doi.org/10.1007/s11760-007-0047-y
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DOI: https://doi.org/10.1007/s11760-007-0047-y