Howtosignalprocessing
- Why inverse filtering approach fails in the presence of noise?
- What is meant by inverse filtering?
- What are the two drawbacks of the inverse filtering?
- What assumptions does inverse filtering make?
Why inverse filtering approach fails in the presence of noise?
Because an inverse filter is a high pass filter, it does not perform well in the presence of noise. There is a definite tradeoff between de-blurring and de-noising. In the following image, the blurred image is corrupted by AWGN with variance 10.
What is meant by inverse filtering?
1. Inverse Filter: Inverse Filtering is the process of receiving the input of a system from its output. It is the simplest approach to restore the original image once the degradation function is known.
What are the two drawbacks of the inverse filtering?
Disadvantages: Noise is amplified at nulls of. Inverse filter may not exist. Inverse filter may be difficult to build.
What assumptions does inverse filtering make?
Assumptions of inverse filtering:
The system is stationary during an analysis interval. The glottal pulse spectrum is flat. The all-pole model of vocal tract characteristics is correct. The estimates of the bandwidths of spectral poles are correct.
