Howtosignalprocessing
- What are the disadvantages of Wiener filter?
- Is Wiener filter a time domain filter?
- Why Wiener filter is called optimal filter?
- Can we use Wiener filters for inverse filtering?
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.
Is Wiener filter a time domain filter?
In signal processing, the Wiener filter is a filter used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise.
Why Wiener filter is called optimal filter?
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.
Can we use Wiener filters for inverse filtering?
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.
