Dull Men's Club
An unofficial chapter of the popular Dull Men's Club.
1. Relevant commentary on your own dull life. Posts should be about your own dull, lived experience. This is our most important rule. Direct questions, random thoughts, comment baiting, advice seeking, many uses of "discuss" rarely comply with this rule.
2. Original, Fresh, Meaningful Content.
3. Avoid repetitive topics.
4. This is not a search engine
Use a search engine, a tradesperson, Reddit, friends, a specialist Facebook group, apps, Wikipedia, an AI chat, a reverse image search etc. to answer simple questions or identify objects. Also see rule 1, “comment baiting”.
There are a number of content specific communities with subject matter experts who can help you.
Some other communities to consider before posting:
5. Keep it dull. If it puts us to sleep, it’s on the right track. Examples of likely not dull: jokes, gross stuff (including toes), politics, religion, royalty, illness or injury, killing things for fun, or promotional content. Feel free to post these elsewhere.
6. No hate speech, sexism, or bullying No sexism, hate speech, degrading or excessively foul language, or other harmful language. No othering or dehumanizing of anyone or negativity towards any gender identity.
7. Proofread before posting. Use good grammar and punctuation. Avoid useless phrases. Some examples: - starting a post with "So" - starting a post with pointless phrases, like "I hope this is allowed" or “this is my first post” Only share good quality, cropped images. Do not share screenshots of images; share the original image.
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(I don't seriously think this is anything that you have to worry about, at all. It's just a neat topic!)
I went down the rabbit hole after watching that video.
It is much easier with text, because the search space is constrained (there's only so many letter/font combinations to look through) you can pixelate each possible character and compare them to the original until you find the closest match.
With enough frames, memory and compute you can extract detailed images through temporal accumulation. Instead of using a lookup table of various characters you can use one of two different methods of motion estimation (Horn-Schunck and Lucas-Kanade [Wikipedia warning: Here thar be mathematics]) and then, once you know the motion, you can use reconstruction algorithms like MAP estimation, or IBP to approximate the high res image that created the low res pixelation. Each frame additional would allow you to refine your 'guess' until you arrived at a unique solution
This would be computationally expensive, on the order of several petaFLOPS and 50-100GB of RAM for even a short video.
A fork of something like this: https://github.com/rafaelmaeuer/MultiFrameSuperResolution taking into account that you don't need to reconstruct the entire image, just a small area.
There are some AI tools that claim to do this as well, but they're just doing image generation and hallucinating the details.