Is your feature request related to a problem? Please describe.
One problem is explained in #3178.
Describe the solution you'd like
Add a median filter based on @ebrahimebrahim's medianblur_with_gpu
Describe alternatives you've considered
#3178 mentions some alternatives, such as mean filtering. Those are suboptimal for many use cases.
Additional context
Model it after GaussianSmooth:
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class GaussianSmooth(Transform): |
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""" |
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Apply Gaussian smooth to the input data based on specified `sigma` parameter. |
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A default value `sigma=1.0` is provided for reference. |
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Args: |
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sigma: if a list of values, must match the count of spatial dimensions of input data, |
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and apply every value in the list to 1 spatial dimension. if only 1 value provided, |
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use it for all spatial dimensions. |
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approx: discrete Gaussian kernel type, available options are "erf", "sampled", and "scalespace". |
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see also :py:meth:`monai.networks.layers.GaussianFilter`. |
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""" |
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backend = [TransformBackends.TORCH] |
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def __init__(self, sigma: Union[Sequence[float], float] = 1.0, approx: str = "erf") -> None: |
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self.sigma = sigma |
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self.approx = approx |
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def __call__(self, img: NdarrayTensor) -> NdarrayTensor: |
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img = convert_to_tensor(img, track_meta=get_track_meta()) |
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img_t, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float) |
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sigma: Union[Sequence[torch.Tensor], torch.Tensor] |
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if isinstance(self.sigma, Sequence): |
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sigma = [torch.as_tensor(s, device=img_t.device) for s in self.sigma] |
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else: |
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sigma = torch.as_tensor(self.sigma, device=img_t.device) |
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gaussian_filter = GaussianFilter(img_t.ndim - 1, sigma, approx=self.approx) |
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out_t: torch.Tensor = gaussian_filter(img_t.unsqueeze(0)).squeeze(0) |
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out, *_ = convert_to_dst_type(out_t, dst=img, dtype=out_t.dtype) |
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return out |
