A High-Resolution Machine Vision System Using Computational Imaging Based on Multiple Image Capture During Object Transport

Abstract

This study adapts Fourier ptychography (FP) for high-resolution imaging in machine vision settings. We replace multi-angle illumination hardware with a single fixed light source and controlled object translation to enable a sequence of slightly shifted low-resolution frames to produce the requisite frequency-domain diversity for FP. The concept is validated in simulation using an embedded pupil function recovery algorithm to reconstruct a high-resolution complex field, recovering both amplitude and phase. For conveyor-belt transport, we introduce a lightweight preprocessing pipeline-background estimation, difference-based foreground detection, and morphological refinement-that yields robust masks and cropped inputs suitable for FP updates. The reconstructed images exhibit sharper fine structures and enhanced contrast relative to native lens imagery, indicating effective pupil synthesis without multi-LED arrays. The approach preserves compatibility with standard industrial optics and conveyor-style acquisition while reducing hardware complexity. We also discuss practical operating considerations, including blur-free capture and synchronization strategies.