Deep learning for NeuroImaging in Python.

Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.

class nidl.volume.transforms.augmentation.intensity.random_blur.RandomGaussianBlur(sigma: tuple[float, float] | tuple[float, float, float, float, float, float] = (0, 2), **kwargs)[source]¶

Bases: VolumeTransform

Blur a 3d volume using a Gaussian filter with random kernel size.

It handles a np.ndarray or torch.Tensor as input and returns a consistent output (same type and shape). Input shape must be $(C, H, W, D)$ or $(H, W, D)$ (spatial dimensions).

Parameters:

sigma : (float, float) or (float, float, float, float, float, float), default=(0, 2)

Range of the standard deviation $\sigma$ of the Gaussian kernel applied to blur the volume. If two values $(a,b)$ are provided, then $\sigma \sim \mathcal{U}(a, b)$ . If six values $(a_1, b_1, a_2, b_2, a_3, b_3)$ are provided, then one standard deviation per spatial dimension is sampled $\sigma_i \sim \mathcal{U}(a_i, b_i)$ for $i=1,2,3$ .

kwargs : dict

Keyword arguments given to base nidl.transforms.Transform.

apply_transform(data: ndarray | Tensor) → ndarray | Tensor[source]¶

Blur the input with a Gaussian filter.

Parameters:: data : np.ndarray or torch.Tensor

Input volume with shape $(C, H, W, D)$ or $(H, W, D)$ . Standard deviations in the Gaussian filter are equal across channels.
Returns:: data : np.ndarray or torch.Tensor

Blurred volume. Output type and shape are the same as input.