CODEWITHSUNDEEP
c++opencvimage-processing
janu777
janu777

Reputation: 1978

blur detection in Images taken with different cameras

I want to detect blurred images using Laplacian Operator. This is the code I am using:

bool checkforblur(Mat img)
{
    bool is_blur = 0;
    Mat gray,laplacianImage;
    Scalar mean, stddev, mean1, stddev1; 
    double variance1,variance2,threshold;
    cvtColor(img, gray, CV_BGR2GRAY);
    Laplacian(gray, laplacianImage, CV_64F);
    meanStdDev(laplacianImage, mean, stddev, Mat()); 
    meanStdDev(gray, mean1, stddev1, Mat()); 
    variance1 = stddev.val[0]*stddev.val[0];
    variance2 = stddev1.val[0]*stddev1.val[0];
    double ratio= variance1/variance2;
    threshold = 90;
    cout<<"Variance is:"<<ratio<<"\n"<<"Threshold Used:"
    <<threshold<<endl;
    if (ratio <= threshold){is_blur=1;}
    return is_blur;
}

This code takes an image as input and returns 1 or 0 based on whether the image is blurred or not. As suggested I edited the code to check for ratio instead of variance of the laplacian image alone.

But still the threshold varies for images taken with different cameras.

Is the code scene dependent?

How should I change it?

Example:

Good

For the above image the variance is 62.9 So it detects that the image is blurred.

Bad

For the above image the variance is 235, Hence it is detecting wrongly as not blurred.

Upvotes: 0

Views: 1424

Answers (2)

David Mendels
David Mendels

Reputation: 116

As suggested above, you should normalize this ratio. Basically, if you divide your variance by the mean value you will get the normalized gray level variance, which I think is what you are looking for.

That said, there is an excellent thread on blur detection which I would recommend - full of good info and code examples.

Upvotes: 0

user1196549
user1196549

Reputation:

The Laplacian operator is linear, so that its amplitude varies with the amplitude of the signal. Thus the response will be higher for images with a stronger contrast.

You might have a better behavior by normalizing the values, for instance using the ratio of the variance of the Laplacian over the variance of the signal itself, or over the variance of the gradient magnitude.

I also advise you to experiment using sharp images that you progressively blur with a wider and wider gaussian, and to look at the plots of "measured blurriness" versus the know bluriness.

Upvotes: 2

Related Questions