Restoration Mask — Dust (512px)
Black-and-white damage mask for the dust restoration case (white = damaged). Use with the damaged input and clean reference.
Specifications
- Width
- 512
- Height
- 512
- Role
- damage mask
- Damage Type
- dust
- Reference
- img-restore-dust-clean
- Suite
- photo-restoration
What is a .png file?
PNG (Portable Network Graphics) is a raster image format using lossless DEFLATE compression. It supports full 8- or 16-bit-per-channel truecolor, palette, and greyscale modes with an optional alpha channel, but no animation. It is the standard choice for screenshots, logos, and graphics with sharp edges or transparency.
How to use this file
Use an example PNG to test image decoders, alpha-compositing, thumbnail generators, and format converters, or to verify that a pipeline preserves transparency and color depth on round-trip.
How to use this file for testing
“Restoration Mask — Dust (512px)” is a deterministic Novus Examples fixture for Photo restoration, Inpainting. Clean references paired with damaged inputs and damage masks — creases, scratches, dust, water stains, torn corners, and cuts — for measuring photo restoration and damage-repair tools against ground truth.
Documented properties for this file: 512×512 · damage mask. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.
Run your enhancement model on the degraded input and diff against the clean ground-truth companion in the same group; keep the documented degradation parameters and seed in your eval notes so scores are reproducible.
Code examples
<img src="dust-mask.png" alt="Example image" width="640" loading="lazy">Related files
- pngRestoration Mask — Crease (512px)Black-and-white damage mask for the crease restoration case (white = damaged). Use with the damaged input and clean reference.
- pngRestoration Mask — Horizontal Cut (512px)Black-and-white damage mask for the horizontal-cut restoration case (white = damaged). Use with the damaged input and clean reference.
- pngRestoration Mask — Scratch (512px)Black-and-white damage mask for the scratch restoration case (white = damaged). Use with the damaged input and clean reference.
- pngRestoration Mask — Torn Corner (512px)Black-and-white damage mask for the torn-corner restoration case (white = damaged). Use with the damaged input and clean reference.
- pngRestoration Mask — Water Stain (512px)Black-and-white damage mask for the water-stain restoration case (white = damaged). Use with the damaged input and clean reference.
- pngInpaint Clean Reference (512px)Clean fruit still-life reference for the inpaint cut suite. Each cut variant and mask in this group points back to this file.
Generated by generation/images_restoration.py. Free for any use, no attribution required — license.