In this paper, we propose an automated adaptive focus pipeline for creating synthetic extended depth of field images using a reflectance transformation imaging (RTI) system. The pipeline proposed detects object regions at different depth levels relative to the camera’s depth of field and collects a most focused image for each. These images are then run through a focus stacking algorithm to create an image where the focus of each pixel has been maximized for the given camera parameters, lighting conditions, and glare. As RTI is used for many cultural heritage imaging projects, automating this process provides high quality data by removing the need for many separate images focused on different regions of interest on the object. It also lowers the skill floor for this image collection process by reducing the amount of manual adjustments that need to be made for focus. Furthermore, this can help to minimize the amount of time that a sensitive cultural heritage object is outside of its ideal preservation environment.
David A. Lewis, Marvin Nurity, Hermine Chatouxz, Fabrice Meriaudeaux, Alamin Mansouri, "An Automated Adaptive Focus Pipeline for Reflectance Transformation Imaging" in Proc. IS&T Int’l. Symp. on Electronic Imaging: 3D Imaging and Applications, 2021, pp 63-1 - 63-7, https://doi.org/10.2352/ISSN.2470-1173.2021.18.3DIA-063