Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Noise Removal in One-Dimensional Signals using Iterative Shrinkage Total Variation Algorithm

Joyce Oliveira dos Santos, Francisco Márcio Barboza

Abstract

The total variation filtering technique emerges as a highly effective strategy for restoring signals with discontinuities in various parts of their structure. This study presents and implements a one-dimensional signal filtering algorithm based on total variation. The aim is to demonstrate the effectiveness of this algorithm through a series of synthetic filtering tests. The results presented in this paper were significant in demonstrating the proposed algorithm's effectiveness. Through a series of rigorously conducted experiments, the algorithm's ability to solve complex noise removal problems in various scenarios was evidenced.

Noise Removal in One-Dimensional Signals using Iterative Shrinkage Total Variation Algorithm

Abstract

The total variation filtering technique emerges as a highly effective strategy for restoring signals with discontinuities in various parts of their structure. This study presents and implements a one-dimensional signal filtering algorithm based on total variation. The aim is to demonstrate the effectiveness of this algorithm through a series of synthetic filtering tests. The results presented in this paper were significant in demonstrating the proposed algorithm's effectiveness. Through a series of rigorously conducted experiments, the algorithm's ability to solve complex noise removal problems in various scenarios was evidenced.

Paper Structure

This paper contains 12 sections, 7 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Fundamentals of One-Dimensional Filtering
  3. Signals
  4. Filtering
  5. Total Variation Filtering
  6. Total Variation Filtering
  7. Iterative Clipping Algorithm
  8. Results and Discussions
  9. Step Signal with Gaussian Noise
  10. Laplace Signal with Gaussian Noise
  11. Noise Removal from the Vuvuzela
  12. Conclusion

Figures (6)

  • Figure 1: Comparison between signals
  • Figure 2: Processing step signals: (a) Step Signal with Gaussian Noise and (b) Step Signal after Filtering.
  • Figure 3: L Curve for the Step Signal
  • Figure 4: Processing Laplace signals: (a) Laplace Signal with Gaussian Noise and (b) Laplace Signal after Filtering.
  • Figure 5: L Curve for the Laplace Signal
  • ...and 1 more figures