784275918-debug/faithful-photo-restoration

Faithful Photo Restoration

Goal

Restore old family photos to a saleable, print-ready standard while preserving the original people. Prefer a conservative faithful result over a visually impressive image that changes faces, adds people, removes people, changes clothing, or invents details.

Core Rule

Treat AI output as a candidate, not the final answer. A candidate is accepted only after visual review against the source. If it changes identity, face structure, people count, clothing, pose, scene relationship, text, or important objects, reject it and use a conservative local restoration.

Workflow

1. Inventory every source image.

   • Keep original files read-only.
   • Assign stable IDs: photo_01, photo_02, etc.
   • Record whether each image is black-and-white, sepia, or color.

2. Preprocess locally before any AI work.

   • Rotate to correct orientation.
   • Crop away phone-shot desk/table/background, album mats, photo frames, white borders, title/date strips, and paper edges unless the user explicitly asks to retain them.
   • Correct perspective only enough to make verticals/horizontals feel like a flat scan.
   • Do not crop into people or important scene content.

3. Produce a conservative local baseline.

   • Use autocontrast, mild denoise/descreen, dust cleanup, and light sharpening.
   • Keep black-and-white photos black-and-white and color photos color unless the user asks for colorization.
   • Use this baseline as the fallback when AI is unsafe.

4. Generate one AI candidate per photo when useful.

   • Use the preprocessed image, not the raw phone photo.
   • Prompt for faithful restoration, not beautification.
   • For large group photos, avoid face enhancement unless the source faces are large enough to compare. Group photos often require conservative local repair.
   • Use prompt templates from references/prompt-templates.md.

5. Review every photo manually.

   • Compare source/preprocessed baseline/AI candidate side by side.
   • Verify people count, face identity, pose, clothing, background relation, and edges.
   • Reject over-clean, plasticky, beautified, or newly generated faces.
   • Read references/quality-gates.md before making accept/reject decisions.

6. Export final deliverables.

   • Final folder: only finished photos, no candidates, no comparison sheets, no scripts, no model cache.
   • Zip: only final photos.
   • Use stable names such as photo_01_7in_300dpi.jpg.
   • If the user wants no border, do not pad to a fixed aspect ratio. Use a 7-inch long edge at 300DPI or another explicit print requirement.

Acceptance Standard

A final image must satisfy all of these:

• Correct orientation; not visibly tilted or phone-shot.
• No desk/table/background/album edge/frame/paper border remains.
• No extra or missing people.
• Faces are clearer but still recognizably the same people.
• No AI glamour, beauty retouching, modernized clothing, or invented texture.
• Damage, haze, scratches, fold marks, and stains are visibly reduced where safe.
• B/W remains B/W; color remains color unless asked otherwise.
• Final package contains only finished photos.

Red Flags

Immediately reject an AI candidate if:

• It makes a small face look like a new person.
• It changes eye shape, hairline, jaw, teeth, beard, wrinkles, or age.
• It makes old paper texture look like a modern studio portrait when the source does not support that detail.
• It sharpens a large group by inventing faces.
• It leaves phone-shot border/background visible.
• It creates a photo that did not exist in the source.

Notes For Agents

• Be explicit when a photo is downgraded to local-conservative because AI changed identity.
• When the user compares two versions, identify which source entered the zip before changing anything.
• Do not delete original source photos. Only clean generated process artifacts after the final zip is verified.
• Zip files do not reduce image quality; if a zip result looks worse, the wrong source image likely entered the zip.