Howtosignalprocessing
- What are the limitations of Wiener filtering?
- How image restoration is done using Wiener filter?
- Why the image is subjected to Wiener filtering?
What are the limitations of Wiener filtering?
Wiener filters are unable to reconstruct frequency components which have been degraded by noise. They can only suppress them. Also, Wiener filters are unable to restore components for which H(u,v)=0. This means they are unable to undo blurring caused by bandlimiting of H(u,v).
How image restoration is done using Wiener filter?
Given a degraded image x(n,m), one takes the Discrete Fourier Transform (DFT) to obtain X(u,v). The original image spectrum is estimated by taking the product of X(u,v) with the Wiener filter G(u,v): The inverse DFT is then used to obtain the image estimate from its spectrum.
Why the image is subjected to Wiener filtering?
It removes the additive noise and inverts the blurring simultaneously. The Wiener filtering is optimal in terms of the mean square error. In other words, it minimizes the overall mean square error in the process of inverse filtering and noise smoothing. The Wiener filtering is a linear estimation of the original image.
