CODEWITHSUNDEEP
pythonopencv
JoeD
JoeD

Reputation: 47

removing black dots from image using OpenCV and Python

I am trying to compare two images and need to pre-process/clean one of them which is a scanned copy before comparing with a digital copy. Scanned copy / Digital copy

I ran this code on the scanned image and got an output which has numerous black dots. Not sure how to clean these up so that I can compare with the digital copy

img = cv2.multiply(img, 1.2)
kernel = np.ones((1, 1), np.uint8)
img = cv2.erode(img, kernel, iterations=1)
kernel1 = np.zeros( (9,9), np.float32)
kernel1[4,4] = 2.0
boxFilter = np.ones( (9,9), np.float32) / 81.0
kernel1 = kernel1 - boxFilter
img = cv2.filter2D(img, -1, kernel1)

below is the output I got

Output Image

Upvotes: 0

Views: 5081

Answers (2)

yapws87
yapws87

Reputation: 1839

Yes. @Andrey method is the right way of solving the problem. I have tried removing the high frequency dots in the frequency domain and here is an example of how it will look like if done correctly

Original Image in grayscale.

enter image description here

After running FFT on the image

enter image description here

Removing all high frequency noise. Of course this is done manually by drawing a black circle around the noise source. You can design your program to detect local bright spot and remove them cleanly.

enter image description here

Here is the final result after inverse FFT of the above frequency image. Some what degraded due to the crude way of me removing the noise but it should give you a rough idea of how it can be done.

enter image description here

Only the area around the dots will be affected by this process, leaving all other pattern in their original form.

Upvotes: 0

Andrey Smorodov
Andrey Smorodov

Reputation: 10852

Try apply filter in frequency domain, your image after FFT will have regular bright dots, because your image noise. If you will remove these dots and make inverse FFT transform you will remove dots from your image. Check this examples please: example1 , example2 and example3.

Upvotes: 2

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