Inpaint Cut — Corner (512px)
Missing-region (corner) input for inpainting / content-aware fill tests. Use with the matching mask and clean reference.
Specifications
- Width
- 512
- Height
- 512
- Role
- cut input
- Cut Type
- corner
- Reference
- img-inpaint-clean
- Suite
- inpaint-cuts
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
“Inpaint Cut — Corner (512px)” is a deterministic Novus Examples fixture for Inpainting, Photo restoration. Images with cut-out regions (rects, circles, corners, strips, irregular tears) and matching masks for testing inpainting, generative fill, content-aware fill, and object-removal pipelines.
Documented properties for this file: 512×512 · cut 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="cut-corner.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 — Dust (512px)Black-and-white damage mask for the dust 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.
Generated by generation/images_inpaint.py. Free for any use, no attribution required — license.