How to detect when an image is out of focus?

Question

At times our optical inspection system gets out of focus which results in nonsensical measurements. I've been tasked to develop an 'out of focus' detector which will be used to drive the Z axis of the camera system. The images available to me are bmp.

I'm looking for approaches and algorithms to investigate. For instance, should I be isolating features and measuring conformance or could edge detection be used?

This is the in focus image:

And this is the out of focus image:

In this situation, do you have several images of the same object and your task is to determine which of the images are out of focus or will you be required to determine if some arbitrary image is out of focus? — Max Allan, Apr 05 '13 at 17:55

sega_sai · Accepted Answer · 2013-04-08T09:23:31.060

12

The key is that in-focus image has much more strong gradients and sharp features. So what I suggest is to apply a Gaussian Laplace filter and then look at the distribution of pixel values of the result. The plot below shows the application of this idea to your images, where black refers to the out of focus image, and red to the one in focus. The in-focus one has much more high values (because the image has more sharp gradients).

When you have the histograms, you can distinguish one from another by comparing e.g. 90%th percentiles of the distributions (which is sensitive to tails). For the out of focus image it is 7 and for the in-focus image it is 13.6 (so twice the difference).

enter image description here

edited Apr 08 '13 at 09:23

answered Apr 05 '13 at 17:51

sega_sai

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How are the values determined? Thanks. – saurabheights May 05 '16 at 07:25
After applying the Gaussian Laplace, how are you building the histogram? Is it the resulting luminance value of pixels? – Michael Austin Jul 03 '19 at 15:44
Yes. The original image was greyscale (single channel) and the application of the filter just gives another image. The histogram shows the distribution of its pixel values. – sega_sai Jul 03 '19 at 18:19

score 1 · Answer 2 · answered Apr 05 '13 at 17:55

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A quick and dirty version of the contrast algorithm is to sum the differences between adjacent pixels - higher sum is more contrast.

answered Apr 05 '13 at 17:55

Martin Beckett

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score 1 · Answer 3 · answered Jun 05 '18 at 15:13

That's what I do in OpenCV to detect the focus quality:

Mat grad;
int scale = 1;
int delta = 0;
int ddepth = CV_8U;
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
/// Gradient X
Sobel(matFromSensor, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT);
/// Gradient Y
Sobel(matFromSensor, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT);
convertScaleAbs(grad_x, abs_grad_x);
convertScaleAbs(grad_y, abs_grad_y);
addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad);
cv::Scalar mu, sigma;
cv::meanStdDev(grad, /* mean */ mu, /*stdev*/ sigma);
focusMeasure = mu.val[0] * mu.val[0];

How to detect when an image is out of focus?

3 Answers3

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