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I'm at the moment trying to implement a neural network which uses BM3D as a preprocessing step. The problem is, when using the python implementation of BM3D from the paper Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance for Improved Shrinkage and Patch Matching it requires that a noise power spectrum (PSD) is passed along with the image to be denoised.

# Generate noise with given PSD
noise, psd, kernel = get_experiment_noise(noise_type, noise_var, seed, y.shape)
# N.B.: For the sake of simulating a more realistic acquisition scenario,
# the generated noise is *not* circulant. Therefore there is a slight
# discrepancy between PSD and the actual PSD computed from infinitely many
# realizations of this noise with different seeds.

# Call BM3D With the default settings.
y_est = bm3d(z, psd) 

The original paper for BM3D, Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, doesn't say anything about passing this PSD beforehand so my question is: What is the point of passing this PSD? In general, images to be denoised have unknown noise levels, shouldn't the denoising algorithm be self contained, in a sense of doing the estimations by itself?

Am I looking at it wrong?

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The original paper uses Wiener filtering, which usually requires knowledge of the expected spectrum.

I haven't read the details of the algorithm (included below), but I suspect the PSD input allows the basic estimate to be avoided.

enter image description here

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    $\begingroup$ I'll have to take a more in depth look at those papers then, as there's so much more work to do then. Thanks a lot! $\endgroup$ Oct 22 '21 at 22:09

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