Build a Restoration Workflow for a Digital Heritage Archive
Overview
What this challenge is about.
You receive 50 high-resolution scans of glass plates plus 3 reference 'gold' restorations done by a senior conservator. Design a reproducible workflow combining inpainting for scratches/spots, density-curve correction, and gentle denoising. Apply it to all 50 pilot images, then have the conservator score each output blind on a 1-5 quality scale. Write a 4-page public-facing methodology note explaining: pipeline steps, what was edited, what was deliberately not edited (e.g., no face inpainting), and reproducibility instructions. Deliver: scripts, restored images, scoring spreadsheet, methodology note.
The Brief
What you'll do, and what you'll demonstrate.
Restore 50 glass-plate scans with a reproducible workflow plus a publishable methodology note that holds up to conservator scrutiny.
Earning criteria — what you'll demonstrate
- Combine inpainting, density correction, and denoising in a reproducible pipeline
- Reason about restoration ethics (what to edit, what to leave)
- Validate restoration quality with a domain-expert blind scoring
- Author a public methodology note that meets archival standards
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Applied AI Scientist
Translating image-processing research into a reproducible, ethically scoped client deliverable is the day-to-day of applied AI scientists at vertical consultancies.
This challenge sharpens
- image-restoration
- process-documentation
- reproducibility
Computer Vision Engineer
Building reproducible classical restoration pipelines is the CV-engineer foundation that complements modern deep-learning work.
This challenge sharpens
- image-restoration
- inpainting
- tone-mapping
AI Safety Researcher
Pre-registering an editing policy and publishing a methodology note is precisely the kind of transparency safety researchers advocate for in generative-AI tooling.
This challenge sharpens
- process-documentation
- computational-imaging
- reproducibility