This really isn't a Fedora question but... Blurring a pixel is simply replacing it with a weighted average of it and the surrounding pixels. The mathematical process is called a "convolution." Classically, in order to deblur an image, you need to know the blurring function (the set of weights on the surrounding pixels, the shape of the group of pixels used in the blur, and the size of the region used in the blur). When done directly, this blurring function is called the "point spread function" for obvious reasons. It turns out that there's a neat thing about this in that the process of blurring is computationally expensive when done directly, but if you do a fourier transform of the image, it's just a multiplication of the image with the blur function (called the "modulation transfer function" in frequency space). Reversing the convolution that resulted in the blurred image is called "deconvolution." The down side of doing things in frequency space is that many of the coefficients are very small, or zero, usually, and 1) you can't divide by zero, 2) small errors in very small numbers lead to very big errors when you divide by them. So, if you don't have your point spread function perfectly characterized, your error blows up. There are all sorts of ways to try to get around this, both in image space and frequency space, but the bottom line is that if you know your point spread function well, you can deblur well, and if you don't, you can't. The well-known "unsharp mask" function is basically one iteration of a multi-iteration deconvolution method in image space that assumes the blur function is a gaussian/binomial. Thus, it doesn't look too bad if you do it a little, but looks horrible if you do it a lot -- because the real blurring function is likely not a gaussian. The more you do it, the more the error becomes dominant. You can try to do this without knowing the blurring function. This is called "blind deconvolution." However, these methods usually force you to make assumptions about things in the image, and then modify the image to fit those things. The classic example here is astronomy photography where you can assume that a distant star it just a dot, or microscopy where they sell tiny little spheres that you then photograph and modify the image so that they look like little spheres. That way you can estimate the point spread function and go from there. Deblurring was a big deal in the 1980s when they put up the Hubble telescope and found out they had polished the mirrors incorrectly. The US government dumped millions of dollars in (successfully) trying to correct for the Hubble blur. It then became very popular in specialized microscopy, such as confocal microscopy, where you are pushing the optics to their limits. The other big advance was for smartphones. There are two common ways to make sure you take good photos. The first is to have excellent lenses with great optics, and try to do as little post processing as possible. The second is to have a cheap lens that is well characterized mathematically and designed to have few of those bad coefficients, so you can easily post process it to get a good image. Thus, you can either buy a really good camera with expensive lenses and take a good picture to start with, or you can put a cheap lousy lens in a smartphone and process the bejeezus out of it. There are a number of freeware programs out there for deconvolution, and a whole industry of proprietary stuff. Unfortunately, you usually have to start by characterizing your point spread function, and that's always a hassle. Some places have standardized point spread functions for well-known lenses, but they can be hard to find. One piece of software that has a number of deconvolution plugins (primarily for microscopy) is ImageJ or Fiji (a not-quite-fork of ImageJ). ImageJ is maintained by the National Institutes of Health in the US, and is free. Fiji is maintained by an academic institution, but I can't remember which. So, do searches on "deconvolution," "blind deconvolution", "deblurring," and "ImageJ deconvolution", "Fiji deconvolution." That will get you started. In particular, see: https://imagej.net/Deconvolution billo On Tue, 2019-07-23 at 01:09 -0700, ToddAndMargo via users wrote: > Hi All, > > Fedora 30, x64 > > Anyone know of a way to remove the blur from this picture? > > https://ibb.co/cTPNHLf > > > Many thanks, > -T > _______________________________________________ > users mailing list -- users@xxxxxxxxxxxxxxxxxxxxxxx > To unsubscribe send an email to users-leave@xxxxxxxxxxxxxxxxxxxxxxx > Fedora Code of Conduct: > https://docs.fedoraproject.org/en-US/project/code-of-conduct/ > List Guidelines: > https://fedoraproject.org/wiki/Mailing_list_guidelines > List Archives: > https://lists.fedoraproject.org/archives/list/users@xxxxxxxxxxxxxxxxxxxxxxx _______________________________________________ users mailing list -- users@xxxxxxxxxxxxxxxxxxxxxxx To unsubscribe send an email to users-leave@xxxxxxxxxxxxxxxxxxxxxxx Fedora Code of Conduct: https://docs.fedoraproject.org/en-US/project/code-of-conduct/ List Guidelines: https://fedoraproject.org/wiki/Mailing_list_guidelines List Archives: https://lists.fedoraproject.org/archives/list/users@xxxxxxxxxxxxxxxxxxxxxxx
