Restoration Damaged — Water Stain (512px)
Damaged input for water-stain restoration testing (seed 411). Pair with the clean reference and damage mask in this group.
Specifications
- Width
- 512
- Height
- 512
- Role
- damaged input
- Damage Type
- water
- Seed
- 411
- Reference
- img-restore-water-stain-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 Damaged — Water Stain (512px)” is a deterministic Novus Examples fixture for Photo restoration, Image pipeline QA. 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 · seed 411 · damaged input. 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="water-stain-damaged.png" alt="Example image" width="640" loading="lazy">Related files
- pngRestoration Clean — Crease (512px)Clean reference for the crease photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-crease.
- pngRestoration Clean — Dust (512px)Clean reference for the dust photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-dust.
- pngRestoration Clean — Horizontal Cut (512px)Clean reference for the horizontal-cut photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-horizontal-cut.
- pngRestoration Clean — Scratch (512px)Clean reference for the scratch photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-scratch.
- pngRestoration Clean — Torn Corner (512px)Clean reference for the torn-corner photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-torn-corner.
- pngRestoration Damaged — Crease (512px)Damaged input for crease restoration testing (seed 408). Pair with the clean reference and damage mask in this group.
Generated by generation/images_restoration.py. Free for any use, no attribution required — license.