The Edge Test: How to Judge AI Photo Cleanup Without Pixel-Peeping

Generative inpainting quality is often evaluated with global similarity metrics, but local boundary continuity is where failures become visible.

Disclosure: I work with Remove Sticker From Photo.

Why edges dominate perception

A removed overlay creates a constrained reconstruction problem. The model must estimate pixels that preserve several local signals at once:

• geometric continuity of lines and contours;
• spatial frequency of texture and grain;
• illumination direction and shadow falloff;
• boundary consistency with untouched pixels.

The output is reconstruction, not recovery of hidden source pixels.

A lightweight QA procedure

1. Render the result at its intended display size.
2. Trace long lines through the edited region.
3. Compare texture frequency inside and outside the mask.
4. Check highlight and shadow direction.
5. Toggle before/after to detect collateral edits.
6. Escalate to a tighter manual mask when automatic selection crosses complex boundaries.

For automated QA, log the edit mask, input dimensions, output dimensions, and provider task ID. Avoid judging only with full-frame perceptual similarity: a small structural break near a high-contrast edge can matter more than a larger low-frequency change in a flat background.

The official workflow guide covers the user-facing process.

Use inpainting only on images you own or may edit. Do not target legitimate attribution, privacy masks, sensitive redactions, or concealed information.