Adaptive Denoising #1066
Adaptive Denoising #1066
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…ns_block functionality. Plus minor changes
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@Garyfallidis Please have a look at this. |
| OUTPUT: | ||
| y - array x will be shifed by m samples down | ||
| along dimension d | ||
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Hi @riddhishb. Please correct docstring to fit the style of our other docstrings.
| block_radius=1, | ||
| rician=True) | ||
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| # Now perform the adaptive soft coefficient matching |
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Remove this inline comment
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Changes Unknown when pulling c3df90d on riddhishb:adap_denoise into * on nipy:master*. |
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| t = time() | ||
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| """ | ||
| The ``ascm`` function takes two denoised inputs, one more smooth than the |
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First sentence has repeated information.
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| The adaptive soft coefficient matching (ASCM) as described in [Coupe11]_ is a | ||
| improved extension of non-local means (NLMEANS) denoising. ASCM gives a better | ||
| denoised images from two standard non-local means denoised versions of the | ||
| original data with different degrees sharp feature preserved. Here, one |
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Replace this with '... original data with different degrees of sharpness. Here, one... '
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Changes Unknown when pulling 13da3b9 on riddhishb:adap_denoise into * on nipy:master*. |
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Changes Unknown when pulling 13da3b9 on riddhishb:adap_denoise into * on nipy:master*. |
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I haven't any further comments on the documentation - for me this can be merged! Please let me know asap if anyone have any comments to this PR. We are planning to merge this at the end of this day! |
This branch is for adaptive denoising, currently includes
for adding a blockwise averaging approach in nlmeans
Adaptive soft coefficient matching based denoising
I have also added the keyword to toggle between current voxelwise implementation of nlmeans and the proposed blockwise one.
I am yet to add tests and some examples.