Paper analyzes persistent homology robustness in denoising 3D images

This paper explores the robustness of persistent homology measures when applied to denoising 3D images, particularly those of porous media. The research investigates how different topological measures, such as bottleneck distance, Wasserstein distance, persistence statistics, and Persistence Images, perform under the influence of Gaussian noise and various denoising techniques, including Gaussian convolution and machine learning approaches. The goal is to assess the reliability of these measures in accurately representing the underlying structure of the images despite noise. AI

IMPACT This research could improve the accuracy of topological data analysis in fields utilizing 3D imaging, potentially impacting AI applications in material science and medical imaging.

RANK_REASON The cluster contains a single academic paper discussing a novel research methodology. [lever_c_demoted from research: ic=1 ai=0.7]

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• arXiv
• bottleneck distance
• Gaussian convolution
• Gaussian noise
• Jonathan Jaquette
• machine learning
• Persistence Images
• persistence statistics
• persistent homology
• Wasserstein distance

• paper
• other

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