Archived

This content is available here for research, reference, and/or recordkeeping.

Author ORCID Identifier

https://orcid.org/0000-0002-5399-8703

Date Available

8-13-2026

Year of Publication

2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Engineering

Department/School/Program

Computer Science

Faculty

William Brent Seales

Faculty

Simone Silvestri

Abstract

Physical restoration of damaged photographic film causes more damage. Also, existing non-invasive digital restoration of film negatives does not produce print-quality optical images. Instead, they produce X-ray projections, which are not the same as optical projections. This thesis addresses these problems and establishes a framework that can digitally restore old, damaged films without the need to open them physically. Virtual Unwrapping is an existing pipeline that has proven itself over the last couple of decades to work on unopenable papyrus scrolls, like the Herculaneum Scrolls. This thesis utilizes the concept of virtual unwrapping to restore damaged photographic film negatives. Due to the chemical composition of the films, over time as they are kept on shelves unattended, they become fragile and stick together, making them hard to open by hand. Using X-ray-based micro-Computed Tomographic reconstruction, a volume of the whole damaged film roll is obtained, showing the internal structure and content. Film rolls are deformed and also suffer from damage like holes. A geodesic automated segmentation algorithm is developed to segment the film roll from the whole volumetric scan. A tree-based hierarchical meshing algorithm is designed to build a mesh, reproducing holes and other deformities. Once the mesh is constructed, it is flattened by using well-known flattening algorithms, like angle-based flattening (ABF) and least-squares conformal mapping (LSCM). The flattened mesh is textured with the X-ray attenuation values from the volume. This X-ray projected flattened output introduces X-ray artifacts like rings, which are not a property of an optical imaging pipeline. So, a UNet-based appearance model is trained on our own collected data and inferred on real-life damaged photographic film from the Museum of Modern Art (MoMA). The appearance model is reported to have achieved a peak signal-to-noise ratio (PSNR) of ~38 dB and a structural similarity (SSIM) of ~0.98.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2026.411

Archival?

Archival

Download

Available for download on Thursday, August 13, 2026

Contact Author

Share

COinS