Detect which image is sharper

I'm looking for a way to detect which of two (similar) images is sharper.

I'm thinking this could be using some measure of overall sharpness and generating a score (hypothetical example: image1 has sharpness score of 9, image2 has sharpness score of 7; so image1 is sharper)

I've done some searches for sharpness detection/scoring algorithms, but have only com开发者_Go百科e across ones that will enhance image sharpness.

Has anyone done something like this, or have any useful resources/leads?

I would be using this functionality in the context of a webapp, so PHP or C/C++ is preferred.

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As e.g. shown in this Matlab Central page, the sharpness can be estimated by the average gradient magnitude.

I used this in Python as

from PIL import Image
import numpy as np

im = Image.open(filename).convert('L') # to grayscale
array = np.asarray(im, dtype=np.int32)

gy, gx = np.gradient(array)
gnorm = np.sqrt(gx**2 + gy**2)
sharpness = np.average(gnorm)

A similar number can be computed with the simpler numpy.diff instead of numpy.gradient. The resulting array sizes need to be adapted there:

dx = np.diff(array)[1:,:] # remove the first row
dy = np.diff(array, axis=0)[:,1:] # remove the first column
dnorm = np.sqrt(dx**2 + dy**2)
sharpness = np.average(dnorm)

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The simple method is to measure contrast -- the image with the largest differences between pixel values is the sharpest. You can, for example, compute the variance (or standard deviation) of the pixel values, and whichever produces the larger number wins. That looks for maximum overall contrast, which may not be what you want though -- in particular, it will tend to favor pictures with maximum depth of field.

Depending on what you want, you may prefer to use something like an FFT, to see which displays the highest frequency content. This allows you to favor a picture that's extremely sharp in some parts (but less so in others) over one that has more depth of field, so more of the image is reasonably sharp, but the maximum sharpness is lower (which is common, due to diffraction with smaller apertures).

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Simple practical approach would be to use edge detection (more edges == sharper image).

Quick and dirty hands-on using PHP GD

function getBlurAmount($image) {
    $size = getimagesize($image);
    $image = imagecreatefromjpeg($image);
    imagefilter($image, IMG_FILTER_EDGEDETECT);    
    $blur = 0;
    for ($x = 0; $x < $size[0]; $x++) {
        for ($y = 0; $y < $size[1]; $y++) {
            $blur += imagecolorat($image, $x, $y) & 0xFF;
        }
    }
    return $blur;
}

$e1 = getBlurAmount('http://upload.wikimedia.org/wikipedia/commons/thumb/5/51/Jonquil_flowers_at_f32.jpg/800px-Jonquil_flowers_at_f32.jpg');
$e2 = getBlurAmount('http://upload.wikimedia.org/wikipedia/commons/thumb/0/01/Jonquil_flowers_at_f5.jpg/800px-Jonquil_flowers_at_f5.jpg');

echo "Relative blur amount: first image " . $e1 / min($e1, $e2) . ", second image " . $e2 / min($e1, $e2);

(image with less blur is sharper) More efficient approach would be to detect edges in your code, using Sobel operator. PHP example (rewriting in C++ should give huge performance boost I guess).

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This paper describes a method for computing a blur factor using DWT. Looked pretty straight forward but instead of detecting sharpness it's detecting blurredness. Seems it detects edges first (simple convolution) and then uses DWT to accumulate and score it.

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Check Contrast Transfer Functions (CTF)

Here's an implementation
Here's an explanation