Optimized generative adversarial network for efficient resolution enhancement of 3D segmented rock tomography.

ABSTRACT: We present a memory-efficient algorithm for significantly enhancing the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks using a Machine Learning (ML) Generative Model. The proposed model achieves a 16 × increase in resolution and corrects inaccuracies in segmentation caused by the overlapping X-ray attenuation in micro-CT measurements across different minerals. The generative model employed is a 3D Octree-Based Progressive Growing Deep Convolutional Wasserstein Generative Adversarial Network with Gradient Penalty (3D OB PG DC WGAN-GP). To address the challenge of extremely high memory consumption inherent in standard PyTorch 3D Convolutional (Conv3D) layers, which is a significant constraint in 3D Super-Resolution (SR) applications, we implemented an Octree str

SUBMITTER: Ugolkov E 

PROVIDER: S-EPMC12559726 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature