Mahmud Yunus

Penerapan Thresholding Bertahap Pada Proses Denoising Sinyal Berbasis Wavelet

Introduction

Penerapan thresholding bertahap pada proses denoising sinyal berbasis wavelet. Pelajari penerapan thresholding bertahap (TSB) untuk denoising sinyal berbasis wavelet. Bandingkan TSB dengan metode Donoho-Johnstone dan FDR pada sinyal 1D. Efisien & menarik.

Abstract

Metode-metode berbasis wavelet menjadi piranti yang sangat e¯sien dan semakin menarik untuk diteliti. Dalam makalah ini disampaikan prosedur penerapan tresholding secara berta- hap (TSB) pada masalah denoising sinyal. Sebagai contoh kasus, TSB akan diterapkan untuk denoising sinyal (data) satu dimensi. TSB diterapkan pada sinyal yang diberikan, dengan terlebih dahulu menentukan nilai threshold yang paling sesuai untuk tiap tahap dekomposisi, kemudian proses denoising dilakukan serentak dengan proses rekonstruksi. Hasil penerapan TSB akan dibandingkan dengan dua metode yang telah cukup dikenal. Pertama, cara Donoho dan Johnstone (1995) yang menerapkan threshold global yang dipilih. Kedua, dibandingkan dengan metode berbasis FDR (False descovery error rate) yang dikemukakan oleh Abramovich dan Benjamini (1998).

Review

This paper presents a novel approach to signal denoising using a wavelet-based method, specifically introducing a "gradual thresholding" (TSB) procedure. Acknowledging the efficiency and growing interest in wavelet techniques, the authors propose TSB as a refined method for noise reduction, exemplified on one-dimensional signals. The core innovation lies in determining the most appropriate threshold value for each decomposition stage, allowing for a more adaptive and potentially precise denoising process that is carried out concurrently with the reconstruction. This adaptive, multi-stage thresholding stands as the primary contribution, aiming to overcome limitations of single-threshold or less nuanced methods. A significant strength of the proposed work, as outlined in the abstract, is its commitment to rigorous comparative analysis. The TSB method will be evaluated against two well-established benchmarks in wavelet denoising: the global thresholding approach by Donoho and Johnstone (1995) and the False Discovery Rate (FDR) based method developed by Abramovich and Benjamini (1998). This direct comparison against highly regarded techniques provides a robust framework for assessing the efficacy and potential advantages of the TSB method. The detailed methodology, emphasizing stage-specific threshold determination and simultaneous denoising-reconstruction, suggests a sophisticated and carefully considered algorithm. Overall, the abstract outlines a promising contribution to the field of signal processing. If the results demonstrate superior or comparable performance to the established methods, particularly in terms of denoising effectiveness and signal fidelity, the gradual thresholding approach could offer a valuable alternative for researchers and practitioners. The paper's focus on a clearly defined problem, a novel algorithmic proposal, and a strong comparative validation strategy positions it as a potentially impactful study in the domain of wavelet-based signal denoising.

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