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    <p>Another very simple way to estimate the sharpness of an image is to use a Laplace (or LoG) filter and simply pick the maximum value. Using a robust measure like a 99.9% quantile is probably better if you expect noise (i.e. picking the Nth-highest contrast instead of the highest contrast.) If you expect varying image brightness, you should also include a preprocessing step to normalize image brightness/contrast (e.g. histogram equalization).</p> <p>I've implemented Simon's suggestion and this one in Mathematica, and tried it on a few test images:</p> <p><img src="https://i.stack.imgur.com/X6Q9d.png" alt="test images"></p> <p>The first test blurs the test images using a Gaussian filter with a varying kernel size, then calculates the FFT of the blurred image and takes the average of the 90% highest frequencies:</p> <pre><code>testFft[img_] := Table[ ( blurred = GaussianFilter[img, r]; fft = Fourier[ImageData[blurred]]; {w, h} = Dimensions[fft]; windowSize = Round[w/2.1]; Mean[Flatten[(Abs[ fft[[w/2 - windowSize ;; w/2 + windowSize, h/2 - windowSize ;; h/2 + windowSize]]])]] ), {r, 0, 10, 0.5}] </code></pre> <p>Result in a logarithmic plot:</p> <p><img src="https://i.stack.imgur.com/CYGCP.png" alt="fft result"></p> <p>The 5 lines represent the 5 test images, the X axis represents the Gaussian filter radius. The graphs are decreasing, so the FFT is a good measure for sharpness. </p> <p>This is the code for the "highest LoG" blurriness estimator: It simply applies an LoG filter and returns the brightest pixel in the filter result:</p> <pre><code>testLaplacian[img_] := Table[ ( blurred = GaussianFilter[img, r]; Max[Flatten[ImageData[LaplacianGaussianFilter[blurred, 1]]]]; ), {r, 0, 10, 0.5}] </code></pre> <p>Result in a logarithmic plot:</p> <p><img src="https://i.stack.imgur.com/cKmdr.png" alt="laplace result"></p> <p>The spread for the un-blurred images is a little better here (2.5 vs 3.3), mainly because this method only uses the strongest contrast in the image, while the FFT is essentially a mean over the whole image. The functions are also decreasing faster, so it might be easier to set a "blurry" threshold.</p>
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    1. POIs there a way to detect if an image is blurry?
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    1. CO+1. Could you please provide a legend for your plots? :P
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    2. COWhat if I'm after the measure of the local blur. Namely, a Photo has areas where it is blurred and where it is sharp. I want to have a map which estimate the blur level per pixel.
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    3. CO@Drazick: I'm not sure if that's even possible. For example, look at the Lena image: There are large areas where there's no contrast (e.g. Lena's skin) although the area is in focus. I can't think of a way to tell if such a smooth area is "blurry", or to distinguish it from an out-of-focus area. You should ask this as a separate question (maybe on DSP.SE). Maybe someone else has better ideas.
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