Local task records
Source copies, results, diagnosis, and QA evidence persist on the computer running the local service.
Damaged photo restoration
Repair severe damage without pretending every detail survived
Severe damage often hides information permanently. Diagnosis should separate visible damage from recoverable signal before any restoration route is chosen.
Local task recordsExternal AI processingQA
External AI processing
Before generation, the product discloses that images may be sent to the configured external service and waits for confirmation.
Review before delivery
Comparison tasks require a preferred result while QA risk and output evidence stay visible.
Restoration evidence
Keep the source, result, and review boundary together
Key principles
damaged photo restoration
Diagnose before restoring
Damage level alone is not enough; facial shadows, outlines, clothing edges, and tonal separation determine what remains defensible.
Match strength to risk
Conservative modes protect weak faces, while balanced modes can recover exposure and separation when enough signal remains.
Escalate uncertain results
Higher identity risk, people-count changes, reconstructed backgrounds, or altered borders should require manual review.
FAQ
Frequently asked questions
Can scratches and stains be removed?
Often yes, when the damage can be separated from real photographic detail.
Can fully missing areas be recovered accurately?
Not with certainty. AI may infer plausible content, which must not be presented as recovered fact.
Why can a clearer result still be risky?
Stronger clarity may come from invented facial or background details rather than recovered source evidence.
Ready to begin
Start reviewing your own photoApply the same review standard to your own old photo.
Upload the source, choose a restoration route, and inspect likeness, composition, and quality evidence before delivery.