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Super Resolution from a Single Image (2009)
- agumonkey 11y agoInteresting to see what some of them did since https://sites.google.com/site/dglasner/ https://sites.google.com/site/dglasner/ Pretty original research.
- jewbear48 11y agoIs there an implementation of this available?
- 0942v8653 11y agoThere is a link in the old thread: https://news.ycombinator.com/item?id=4242002 https://news.ycombinator.com/item?id=4242002
- CamperBob2 11y agoI guess I fail the IQ test. Where's the download link?
- XaspR8d 11y agoIt looks like the page header/navigation got lost in time. Archive.org shows a lot of now-missing content [1], perhaps in the move to GitHub Pages? In any case the download page does still exist: http://mentat.za.net/supreme/download.shtml http://mentat.za.net/supreme/download.shtml Which leads to the repo: https://github.com/stefanv/supreme https://github.com/stefanv/supreme [1] https://web.archive.org/web/20140204102520/http://mentat.za.net/supreme/index.shtml https://web.archive.org/web/20140204102520/http://mentat.za....
- dang 11y agoDiscussed in 2012: https://news.ycombinator.com/item?id=4241266 https://news.ycombinator.com/item?id=4241266.
- radarsat1 11y agoSo, if I understand from the abstract, for every pixel they look through the image for similar "pixel neighbourhoods" of the same or varying size, and collect them into a kind of database of examples to be used for scaling up that pixel. Pretty cool idea!
- CephalopodMD 11y agoZoom and enhance
- turshija 11y agoI just can't understand how it manages to make this image http://www.pics.rs/i/2cuoM http://www.pics.rs/i/2cuoM look like this image http://www.pics.rs/i/IEz0N http://www.pics.rs/i/IEz0N, especially the last line of letters.
- vog 11y agoI guess the key reason why this works so well is because the lower resolution has been calculated from the higher resolution. So you have detailed information on how exactly the downscaling was calculated, which enables you to "cheat". If you apply the same method to a completely different downscaling method, it probably wouldn't work out as nicely. The ultimate test would be two different photographs taken at the same time - one with high and one with low resolution. Almost certainly this wouldn't work out as nicely. There is a huge difference between (a) having a low-res image due to physical effects, and (b) having a low-res image calculated by a known algorithm where all parameters are also known.
- jessriedel 11y agoVery true, but in principle it's possible to completely characterize the downscaling process effected by a physical camera.
- vog 11y agoGood point. In this case it is essentially the same as using a known algorithm with unknown parameters. Either way, this increases the space of possible upscaled images dramatically, which makes it a lot harder (if not impossible) to produce result of the demonstrated quality.
- beagle3 11y agoThis is plain wrong. As others have pointed out, it is independent of the scaling method - it just looks for similar patches (at different sizes, mirror images, and perhaps even rotations - I don't remember) on the same image to find the best one that matches, and uses that for information. In this specific case, you have all the letters there to match against, so there's no surprise. Note that it uses the entire image to look for matches. If you tried to enlarge only the last line, it would probably NOT look as good. There's a good reason that it works: Many pictures include similar elements at different scales, which lets you infer things from one scale to the other. In fact, in the eighties there was a lot of hype about "fractal compression" based on the same principle, see e.g. http://www.cs.northwestern.edu/~agupta/_projects/image_processing/web/FractalImageCompression/ http://www.cs.northwestern.edu/~agupta/_projects/image_proce... ; In the end, they couldn't improve on JPEG, and has been essentially forgotten - but ... it did match the JPEG coders at the time in terms of compression rate (did much worse on speed and memory requirements); and, it could decompress pictures to much larger geometry than the original while still looking good -- technically, very similar to what is described in this paper. Everything old is new again.
- colanderman 11y agoI wonder if the same techniques could be used for compression as well.
- irixusr 11y agoIf you mean lossy, why not? Netflix could use this by reducing the information they send out to their users who recreate a lot of what was lost. I don't think for loss-less though, because they are inferring what a pixel should to be, i.e. they cannot be 100% any bit is every set right, just the probability that it is.
- foota 11y agoI believe this is already somewhat similar to say zip compression, which can reference other portions of the compressed file.
- Houshalter 11y agoAbsolutely. For lossless compression you just need to encode the difference between the predicted pixels and the actual pixels. Which hopefully has a lot of zeros, making it easier to compress. For lossy compression, you just throw away some or all of the difference info. The biggest issue is speed. This doesn't look very fast to encode or decode. That's the biggest issue with new image compression algorithms, and makes it nonviable for most purposes.
- nitrogen 11y agoSome photocopiers have actually replaced numbers with different numbers in copied documents because of pattern-based compression: https://news.ycombinator.com/item?id=6156238 https://news.ycombinator.com/item?id=6156238
- ansgri 11y agoPhotocopiers use Mixed Raster Content (MRC) with JBIG compression, which is indeed able to confuse symbols at the same scale, but across-the-scales substitution is beyond the current MRC capabilities afaik.
- happyindeed 11y agoIt has a visual style that looks like a "photorealism" painting. Quite amazing. https://en.wikipedia.org/wiki/Photorealism https://en.wikipedia.org/wiki/Photorealism
- coldtea 11y agoHmmm? The idea of photorealism is that it should look as much as an actual photograph (or reality) as possible. And the best such painters, do.
- crimsonalucard 11y agoThis is the algorithm they use in all those movies. "Enhance!"
- zurn 11y agoOriginal code @ https://github.com/stefanv/supreme https://github.com/stefanv/supreme (repost from downthread). Looks like it's BSD licensed and in Python.
- ansgri 11y agoAs I see from http://mentat.za.net/supreme/ http://mentat.za.net/supreme/, this is an implementation of wholly another method, neither single-image nor self-similarity–based.
- osdf 11y agoA lot of things happened since then. References up to spring 2014 are e.g. in our own work to super-resolve arbitrary sized images with convolutional en/decoders: Super-Resolution with Fast Approximate Convolutional Sparse Coding, http://brml.org/uploads/tx_sibibtex/281.pdf http://brml.org/uploads/tx_sibibtex/281.pdf (which still has a lot of possibilities to be extended (e.g. color) and improved upon).
- nkoren 11y agoInteresting paper; I'd be curious to see examples of how that performs. Are any online?
- tmaly 11y agoTo clarify my understanding of this post, would it be possible using this method to: 1. start with a high resolution image 2. create a low resolution version 3. using the low resolution version produce a high resolution version that looks good