Techniques and Algorithms to Tackle Impulse Noise
- The most preferred Median, adaptive and bilateral filtering technique.
- Wavelet denoising for reconstructing the denoised signal.
- Algorithms as Least Median of Squares or Least Trimmed Squares (LTS) estimator are statistical techniques used to estimate signal parameters in the presence of outliers, such as impulse noise.
- Error correction codes such as Reed-Solomon codes or convolutional codes are used to detect and correct errors introduced by impulse noise during data transmission.
- Also to suppress the impulse noise, the threshold value is adaptively adjusted.
Impulse Noise
The term noise usually describes undesirable disturbances or fluctuations and is considered to be the worst case for communication and error-free information transmission and processing in engineering. The noises you hear throughout the day can be either continuous noise, intermittent noise, impulsive noise, or low-frequency noise.
In this article, we will go through the definition of impulse noise, its types, models, examples, differences or comparisons with typical noise, applications, techniques or algorithms to measure, advantages, disadvantages, conclusion and FAQs.
Table of Content
- Definition
- Types
- Examples
- Noise Vs Impulse Noise
- Applications
- Techniques
- Advantages
- Disadvantages:
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