A 3D denoising machine learning model using Vision Transformer (ViT) brings powerful capabilities to image restoration tasks. Unlike traditional CNNs, ViT captures long-range dependencies in 3D volumetric data, making it ideal for reducing noise in medical imaging, LiDAR scans, and 3D reconstructions. By leveraging self-attention mechanisms, ViT learns spatial features across all dimensions, enhancing image clarity without losing detail. This approach outperforms conventional methods in denoising quality and generalization, offering a robust, scalable solution for real-world 3D imaging applications where precision matters most.
