Fast Factorized Backprojection Algorithm for UWB Bistatic.pdf
Filtered Backprojection Algorithm in MATLAB.ppt
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Transcript of Filtered Backprojection Algorithm in MATLAB.ppt
Filtered Backprojection Algorithm
1. Measure the family of projections
K = # of projections (angles), N=# of rays (detector size)
2. Perform FFT[ ]
3. Multiply FFT[ ] by FFT[ ].
4. Perform IFFT[ product from step 3]
5. Perform backprojection as:
NnKinPi
,...,2,1;,...,2,1,
KinPi
,...,2,1,
nPi
nh
K
iii yxQ
Kyxf
i1
sincos,
1) Measure the family of projections
[R,Xp] = RADON(...) returns two variables Matrix R – columns are the
Radon transform for the angles . Rows are detector position
Vector Xp - containing the radial coordinates corresponding to each row of R.
1) Measure the family of projections
Function parameters size(): gets N and K nextpow2(): get width for FFT
2) Perform FFT[ ]
MATLAB stands for MATrix LABoratory Matrix operations are faster than visiting
each element in a loop.
KinPi
,...,2,1,
3) Multiply FFT[ ] by FFT[ ]
Backprojection filters Shepp-Logan: Ram-Lak
multiplied by sinc function (see iradon help)
Cosine: Ram-Lak multiplied by cosine
Hamming: Ram-Lak multiplied by Hamming window
Hanning, Blackman, etc.
nh nPi
3) Multiply FFT[ ] by FFT[ ]
FIR Filters. Commonly used Windows
nh nPi
Figure from “Discrete-Time Signal Processing”, Oppenheim & Schafer, Prentice-Hall
3) Multiply FFT[ ] by FFT[ ]
Rectangular
Bartlett (triangular)
Hanning (von Hann)
Hamming
Blackman
nh nPi
otherwise
Mnnw
,0
,0,1][
otherwise
MnMMn
MnMn
nw
,0
2/,/22
2/0,/2
][
otherwise
MnMnnw
,0
,0),/2cos(5.05.0][
otherwise
MnMnnw
,0
,0),/2cos(46.054.0][
otherwise
MnMnMnnw
,0
,0),/4cos(08.0)/2cos(5.042.0][
from “Discrete-Time Signal Processing”, Oppenheim & Schafer, Prentice-Hall
3) Multiply FFT[ ] by FFT[ ]
Filter step. Element by element multiplication
nh nPi
4) IFFT[product from step 3]