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

TL;DR

This paper introduces an iterative shrinkage total variation algorithm for effectively removing noise from one-dimensional signals, demonstrating its success through synthetic tests and experiments.

Contribution

The paper proposes a novel iterative shrinkage total variation algorithm specifically designed for one-dimensional signal noise removal.

Findings

Effective noise removal demonstrated in synthetic tests

Algorithm successfully handles complex noise scenarios

Significant improvement over traditional methods

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.