Search for collections on FTS Digilib

A Cubical Persistent Homology-Based Technique for Image Denoising with Topological Feature Preservation

Al-Imran, Md. and Liza, Mst Zinia Afroz and Shiraj, Md. Morshed Bin and Murshed, Md. Masum and Akhter, Nasima (2024) A Cubical Persistent Homology-Based Technique for Image Denoising with Topological Feature Preservation. Journal of Computing Theories and Applications, 2 (2). pp. 222-243. ISSN 3024-9104

[thumbnail of 11488-Article Text-40369-1-10-20241006.pdf] Text
11488-Article Text-40369-1-10-20241006.pdf - Published Version

Download (1MB)

Abstract

Image denoising is a fundamental challenge in image processing, where the objective is to remove noise while preserving critical image features. Traditional denoising methods, such as Wavelet, Total Variation (TV) minimization, and Non-Local Means (NLM), often struggle to maintain the topological integrity of image features, leading to the loss of essential structures. This study proposes a Cubical Persistent Homology-Based Technique (CPHBT) that leverages persistence barcodes to identify significant topological features and reduce noise. The method selects filtration levels that preserve important features like loops and connected components. Applied to digit images, our method demonstrates superior performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 46.88 and a Structural Similarity Index Measure (SSIM) of 0.99, outperforming TV (PSNR: 21.52, SSIM: 0.9812) and NLM (PSNR: 22.09, SSIM: 0.9822). These results confirm that cubical persistent homology offers an effective solution for image denoising by balancing noise reduction and preserving critical topological features, thus enhancing overall image quality.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User: dl fts
Date Deposited: 17 Nov 2024 16:39
Last Modified: 04 Dec 2024 01:22
URI: https://dl.futuretechsci.org/id/eprint/11

Actions (login required)

View Item
View Item