Howtosignalprocessing
- What are the disadvantages of Wiener filter?
- Are Wiener filters optimal?
- What does a Wiener filter do?
- In which condition does the Wiener filter reduces to inverse filter?
What are the disadvantages of Wiener filter?
From the foregoing discussion of filters that are generalizations of the simple Wiener filter, a major disadvantage is apparent: the power spectra of the random fields to which picture and noise are assumed to belong must be known or estimated.
Are Wiener filters optimal?
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. The approach is based on a stochastic framework.
What does a Wiener filter do?
The Wiener filter can be used to filter out the noise from the corrupted signal to provide an estimate of the underlying signal of interest. The Wiener filter is based on a statistical approach, and a more statistical account of the theory is given in the minimum mean square error (MMSE) estimator article.
In which condition does the Wiener filter reduces to inverse filter?
Note that at spatial frequencies where the signal-to-noise is very high, the ratio RN(u, υ)/ RI(u, υ) approaches zero, and the Wiener filter reduces to the inverse filter. However, when the signal-to-noise ratio is very poor (i.e., RN(u, υ)/ RI(u, υ) is large), the estimated spatial frequencies approach zero.
