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Mean Std Normalization

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Zero-mean and unit-variance normalization.

MeanStdNormalization

Bases: Normalization

Zero-mean and unit-variance normalization.

The normalization expects arrays of dimensions C(Z)YX.

Parameters:

  • input_means (list[float]) –

    Mean values (length 1 for global, multiple values for per channel).

  • input_stds (list[float]) –

    Standard deviation values (length 1 for global, multiple values for per channel).

  • target_means (list[float] | None, default: None ) –

    Target mean values (length 1 for global, multiple values for per channel), by default None.

  • target_stds (list[float] | None, default: None ) –

    Target standard deviation values (length 1 for global, multiple values for per channel), by default None.

  • skip_target (bool, defaut=False, default: False ) –

    When True, target normalization is skipped and the target is returned unchanged.

__call__(patch, target=None)

Apply the transform to the source patch and the target (optional).

Parameters:

  • patch (ndarray) –

    Patch, 2D or 3D, shape C(Z)YX.

  • target (ndarray, default: None ) –

    Target for the patch, by default None.

Returns:

  • tuple of numpy.ndarray –

    Transformed patch and target, the target can be returned as None.

__init__(input_means, input_stds, target_means=None, target_stds=None, skip_target=False)

Constructor.

Parameters:

  • input_means (list[float]) –

    Mean values (length 1 for global, multiple values for per channel).

  • input_stds (list[float]) –

    Standard deviation values (length 1 for global, multiple values for per channel).

  • target_means (list[float] | None, default: None ) –

    Target mean values (length 1 for global, multiple values for per channel), by default None.

  • target_stds (list[float] | None, default: None ) –

    Target standard deviation values (length 1 for global, multiple values for per channel), by default None.

  • skip_target (bool, defaut=False, default: False ) –

    When True, target normalization is skipped and the target is returned unchanged.

denormalize(patch)

Reverse the normalization operation for a batch of patches.

The data is denormalized using the target statistics, when available. When no target statistics are available, which is the case for self-supervised algorithms such as N2V, the data is denormalized using the input statistics.

If self.skip_target is True, then this method returns patch.

Parameters:

  • patch (Tensor) –

    Patch, 2D or 3D, shape BC(Z)YX.

Returns:

  • Tensor –

    Denormalized array.