Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information

Understand

The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.

• While deep learning has shown success in controlled environments, its application to complex geological materials under conditions of incomplete information remains underexplored.
• This study presents an integrated framework for the inpainting and classification of high-resolution core sample images.
• We propose an end-to-end pipeline that utilizes object detection for sample segmentation, followed by image inpainting using Generative Adversarial Networks (GANs) with Contextual Residual Aggregation (CRA) to reconstruct missing high-frequency details.