Tutorial 37 - Image filtering in python - Block matching and 3D filtering (BM3D) for image denoising

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In microscopy, Gaussian noise arises from many sources including electronic components such as detectors and sensors. In addition, salt & pepper noise may also show up due to errors in analog to digital conversion. Therefore, image denoising is one of the primary pre-processing operations that a researcher performs before proceeding with extracting information out of these images.

References:

  1. ZEISS arivis
  2. image analysis
  3. image processing
  4. python
  5. denoising
  6. block matching
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Tutorial 37 - Image filtering in python - Block matching and 3D filtering (BM3D) for image denoising

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Комментарии
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Thank you very much for you effort . I do really have alot of new information

JwanKAlwan
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Thankyouuu so much sir
It helped me a lottt

nishantgoyal
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This is a really nice series and I've become super enthusiastic about python data treatment. I'd like to know how Sreeni manages to get Ipython to output "real size" images? Same with the configuration of cv2.imshow()... the popup windows in my screen are always huge, please, help?

aitorthaddeus
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Sadly I'm unable to find a Mac version of Zen. But I went for FIJI- imageJ, hope it has this functionality to add containers for python code.

amith
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Sir when I ran this code i got the following error "AttributeError: module 'bm3d' has no attribute 'BM3DStages'". Can you please help me resolve it?

anilaannegeorge
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Is there anyone who encounters the problem where the pixel values become more than 1.0 even though dtype = float64 after running BM3D?

OhmFF
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