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Wiener filtering illustrations
6.011, Spring 2018 Lec 21
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Unconstrained Wiener filter structure
- mx
my + + x[n] h[·] y[n]
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Wiener filtering illustrations 6.011, Spring 2018 Lec 21 1 - - PowerPoint PPT Presentation
Wiener filtering illustrations 6.011, Spring 2018 Lec 21 1 - - PowerPoint PPT Presentation
Wiener filtering illustrations 6.011, Spring 2018 Lec 21 1 Unconstrained Wiener filter structure - m x m y y [ n ] x [ n ] + + h [] 2 Unconstrained Wiener filter solution - m x m y D yx ( e j ) H ( e j ) = x [ n ] y [ n ] + + D xx (
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Unconstrained Wiener filter solution
- mx
my y[n] H(ejÆ) = Dyx(ejÆ) Dxx(ejÆ) + + x[n]
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E.g.: Wiener “deconvolution”
- f a noisy blurred signal
v[n] y[n] r[n] x[n] H[z] G[z] y[n] + Known, stable system Wiener flter
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E.g.: Wiener deconvolution
- f a noisy blurred image**
Two-dimensional convolution + noise:
x[k, l] = P P
j g[i, j]y[k − i, l − j] +v[k, l] i
**From 2007 Mathworks blog post by
- Prof. Stan Reeves, ECE Dept., Auburn University
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Wiener deconvolution of a noisy blurred image
Mathworks blog posts by:
- Prof. Stan Reeves, ECE Dept., Auburn University
Reeves, Stan. "Digital image processing using MATLAB: reading image files". MathWorks. Sept. 27, 2011. Reeves, Stan. "Image deblurring – Wiener filter." MathWorks.
- Nov. 2, 2007.
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MIT OpenCourseWare https://ocw.mit.edu
6.011 Signals, Systems and Inference
Spring 2018 For information about citing these materials or our Terms of Use, visit: https://ocw.mit.edu/terms.
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