KoKuToru/de-pixelate_gaV-O6NPWrI resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1506 stars · 152 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Jupyter Notebook-based proof-of-concept that reconstructs pixelated (mosaic-censored) content from a YouTube video by combining information across frames as the pixelated window moves. It demonstrates a de-pixelation technique using frame extraction, edge detection, and grid alignment.

Use cases

• de-pixelate a censored video
• recover content hidden behind a mosaic blur
• learn how mosaic removal from moving video works
• reconstruct pixelated screen content from multiple frames
• study a computer vision proof of concept for censor removal

When to choose

• you want to understand or experiment with mosaic de-pixelation from video frames
• you have a video where the pixelated region moves relative to static underlying content
• you want a small educational example with notebooks to follow

When to avoid

• you need a robust, production-ready de-pixelation tool
• you expect a general-purpose image restoration library with an API
• your target video has a static pixelated region with no movement to exploit
• you need a maintained project with a clear license for all files

Facets

learning-resource · maturity experimental

image-processing computer-vision video-processing computer-vision image-processing python cross-platform de-pixelation mosaic-reconstruction jupyter-notebook proof-of-concept super-resolution video

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markdown · JSON · MCP: product_card(name="KoKuToru/de-pixelate_gaV-O6NPWrI")