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Joined 3 months ago
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Cake day: January 26th, 2026

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  • DLSS is essentially an advanced interpolation algorithm, it makes a guess of what should be in between two know pixel values. This can be very useful for human operators who need to look at the data. It also has the advantage that you only need a trained model and one image frame at a time. Some ‘superresolution’ methods essentially do this, but ideally you don’t use this until after you’ve applied mathematically correct techniques.

    Superresolution methods exist in many forms. Basically all of them require either some prior knowlegde (or assumption) of what you’re looking at or it takes a lot of data. But once you have this, you can go beyond the optical resolution of your system in a mathematically correct way, you don’t have to guess!

    Some examples:

    • Lens correction: it’s possible to determine how imperfections in your lens affect the image, then correct for this. With this prior lens knowledge your images will be nearly as good as those from a theoretical perfect lens. However, you’re still limited by the (diffraction limit) laws of physics, regardless of how (im)perfect your lens is.
    • Deconvolution: from physics it’s known how light diffracts (bends) and how this leads to optical limitations. Through deconvolution you can undo this. This takes a lot of guess work to find the correct solution, but once you have the solution, you can check that it’s mathematically correct (it’s a bunch of fancy integrals).
    • Using information of multiple pixels v1: if an object in your image consists or more than one pixel, you have more information to determine where exactly this object is. If you know the shape of the object (e.g. a circle) you can make a fit to it and determine some properties extremely accurately (e.g. the circle center of a 1 μm particle can routinely be determined to a 10 nm resolution by a microscope that has an optical resolution of 200 nm). This method requires prior knowledge of the shape! Planes and oil storage tanks have known shapes…
    • Using information of multiple pixels v2: theoretically you just need more information to go beyond the optical resolution. This can be done by taking many images (from slightly different positions?) of the same field of view. I don’t know how this works, but I have no doubt that there are people doing this.












  • There’s plenty of perfectly fine distributions out there. Mint is an easy choice, easy to get started with, big community that probably already has answered the questions you might have and otherwise you can ask them. Many more gaming focused people use Bazzite, not sure what it offers on top of a basic, well working environment.

    The Nvidea graphics card could cause issues since drivers tend to not be supported well. Again, you’re most likely to find help for the bigger distros such as Mint and Bazzite.

    Regardless of which distro you choose, just try it and see how it goes. Dual boot can be a nice starting point (but make sure you get the partitioning right before installing anything!).


  • So far the USA still provides a competitive funding environment, despite the budget cuts. I suspect that the increasingly limited academic freedom due to the fascist government will become the greater driver of migration.

    I’m under the impression that not many people have moved yet and that it’s mostly people returning home (which isn’t necessarily a bad thing, they’re great researchers too), but of course acquiring funding takes a while. We’ll have to see how it continues in the next few years.