📅 06 October 2024
DOI: 10.62411/jcta.11488
A Cubical Persistent Homology-Based Technique for Image Denoising with Topological Feature Preservation
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 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.
🔖 Keywords
#Cubical Complex; Image Analysis; Persistent Homology; Sublevel Set Filtration; Topological Data Analysis
ℹ️ Informasi Publikasi
Tanggal Publikasi
06 October 2024
Volume / Nomor / Tahun
Volume 2, Nomor 2, Tahun 2024
📝 HOW TO CITE
Al-Imran, Md.; Liza, Mst Zinia Afroz; Shiraj, Md. Morshed Bin; Murshed, Md. Masum; Akhter, Nasima, "A Cubical Persistent Homology-Based Technique for Image Denoising with Topological Feature Preservation," Journal of Computing Theories and Applications, vol. 2, no. 2, Oct. 2024.
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