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If padding L-1 zeros were the only thing that's done for upsampling, nobody would actually store the end result with zeros that way, because the zeros would be redundant. In practice, padding with L-1 zeros is always followed by low pass filtering. If that filtering is done with an FIR filter, a dumb implementation would have many coefficients being ...


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Often it's possible to detect invalid markers or Huffman decompression errors. If bottom part is grey though, it's most likely invalid marker inside JPEG image data. JPEGSnoop will tell exact byte, edit offending FF xx marker using HxD for example, and rest of image will decode (unless corruption is more extensive). In below example I edit one byte only (FF ...


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I ran the following code: clear(); close('all'); mI = im2double(imread('bCfdb.png')); %<! Loading the image vBlurStd = [0, 0.1, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2]; mII = sum(cat(3, 0.299, 0.587, 0.114) .* mI, 3); %<! Y (Luminosity like channel) hFigure = figure('Position', [100, 100, 1200, 900]); hTiledChartLayout = tiledlayout(3, 3); kk = 0; for ii =...


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Very interesting problem I will focus on the second aspect of your question, how to improve it. What I would do next, based on the experience you shared is to combine the two methods. One problem of your first approach is that it accumulates the errors in the alignment between multiple images. A possible approach would be to use your focus measure to align ...


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Assuming modern 3D games here. most games you are aware of are not fixed-resolution – there's very rarely a 1080p game these days! The GPU only renders things onto a buffer the size of your screen's resolution when asked to (in the last step of the processing chain, basically), so games can simply have a fully relative-coordinate-system going on till the end....


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"Image processing method" here really boils down to ... "for" loops. You'd walk the main diagonal (or the first non-main diagonal next to it), and as soon as you hit a value, you count the values right of that aren't 0. Soon as you know how many these are, you're done. Half that number, add it to the number of the row, and tadah, center ...


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When processing noisy data, it is usually beneficial to filter high frequencies, this can be done with a small kernel as shown in answer by Dan Boschen. Also if your data has an envelope it will give you some high intensity low frequencies, the low frequencies require a long kernel to be filtered, and it is better to do in the frequency domain. The topic of ...


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All of the answers above contained valuable insight, but didn't succinctly answer the practical question: how do I choose sigma? Often the practical question is posed in the form that a trade-off needs to be found between quality of results (Low-pass filtering, noise suppression) and implementation complexity. Suppose a particular HW architecture supports 2D ...


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