A 3D Denoising Neural Network for Improved Surface Quality in Inline CT Metrology

Inline computed tomography (CT) is becoming increasingly important for 100% inspection in production technology. Rapid scanning cycles, often limited to minutes, require a reduction in the number of X-ray projections, resulting in significantly reduced photon counts compared to laboratory-based systems. This reduction adversely impacts the peak signal-to-noise ratio (PSNR), increasing noise levels and compromising the accuracy of surface measurements essential for dimensional metrology. To address this challenge, we use neural network-based denoising techniques that enhance image quality without the need for clean reference data. Additionally, we introduce a novel surface quality assessment metric based on the Hausdorff distance, enabling real-time monitoring of surface accuracy during CT scans. Our approach uses a three-dimensional U-Net architecture and an innovative data augmentation method to optimize denoising performance. Experimental results demonstrate that the proposed methods significantly improve surface quality and PSNR in CT data within the time constraints necessary for real-time applications.