Normalize

Normalization and denormalization transforms for image patches.

`Denormalize`

Denormalize an image.

Denormalization is performed expecting a zero mean and unit variance input. This transform expects C(Z)YX dimensions.

Note that an epsilon value of 1e-6 is added to the standard deviation to avoid division by zero during the normalization step, which is taken into account during denormalization.

Parameters:

Name	Type	Description	Default
`image_means`	`list or tuple of float`	Mean value per channel.	required
`image_stds`	`list or tuple of float`	Standard deviation value per channel.	required

`Normalize`

Bases: Transform

Normalize an image or image patch.

Normalization is a zero mean and unit variance. This transform expects C(Z)YX dimensions.

Not that an epsilon value of 1e-6 is added to the standard deviation to avoid division by zero and that it returns a float32 image.

Parameters:

Name	Type	Description	Default
`image_means`	`list of float`	Mean value per channel.	required
`image_stds`	`list of float`	Standard deviation value per channel.	required
`target_means`	`list of float`	Target mean value per channel, by default None.	`None`
`target_stds`	`list of float`	Target standard deviation value per channel, by default None.	`None`

Attributes:

Name	Type	Description
`image_means`	`list of float`	Mean value per channel.
`image_stds`	`list of float`	Standard deviation value per channel.
`target_means`	`list of float, optional`	Target mean value per channel, by default None.
`target_stds`	`list of float, optional`	Target standard deviation value per channel, by default None.

`TrainDenormalize`

Denormalize an image tensor for training-time tensors.

This class mirrors Denormalize but operates on torch tensors. It expects the input tensor to have shape BC(Z)YX with the channel dimension at index 1.

Parameters:

Name	Type	Description	Default
`image_means`	`list or tuple of float`	Mean value per channel.	required
`image_stds`	`list or tuple of float`	Standard deviation value per channel.	required