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README.rst

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@@ -26,6 +26,8 @@ Pytorch Wavelet Toolbox (`ptwt`)
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:target: https://github.com/psf/black
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:alt: Black code style
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.. image:: https://static.pepy.tech/personalized-badge/ptwt?period=total&units=international_system&left_color=black&right_color=brightgreen&left_text=Downloads
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:target: https://pepy.tech/project/ptwt
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@@ -145,7 +147,7 @@ Reconsidering the 1d case, try:
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The process for the 2d transforms ``MatrixWavedec2``, ``MatrixWaverec2`` works similarly.
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By default, a non-separable transformation is used.
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To use a separable transformation, pass ``separable=True`` to ``MatrixWavedec2`` and ``MatrixWaverec2``.
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Separable transformations use a 1d transformation along both axes which might be faster since less matrix entries
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Separable transformations use a 1d transformation along both axes, which might be faster since fewer matrix entries
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have to be orthogonalized.
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- Adaptive product-filters
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- and optimizable orthogonal-wavelets are supported.
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See https://github.com/v0lta/PyTorch-Wavelet-Toolbox/tree/main/examples for a complete implementation.
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See https://github.com/v0lta/PyTorch-Wavelet-Toolbox/tree/main/examples/network_compression/ for a complete implementation.
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**Testing**

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