tfmri.losses.SSIMMultiscaleLoss

class SSIMMultiscaleLoss(max_val=None, power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333), filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03, batch_dims=None, image_dims=None, rank=None, multichannel=True, complex_part=None, reduction='auto', name='ssim_multiscale_loss')[source]

Bases: tensorflow_mri.python.losses.iqa_losses.LossFunctionWrapperIQA

Computes the multiscale structural similarity (MS-SSIM) loss.

The MS-SSIM loss is equal to \(1.0 - extrm{MS-SSIM}\).

Parameters

  • max_val – The dynamic range of the images (i.e., the difference between the maximum and the minimum allowed values). Defaults to 1 for floating point input images and MAX for integer input images, where MAX is the largest positive representable number for the data type.

  • power_factors – A list of weights for each of the scales. The length of the list determines the number of scales. Index 0 is the unscaled resolution’s weight and each increasing scale corresponds to the image being downsampled by 2. Defaults to (0.0448, 0.2856, 0.3001, 0.2363, 0.1333), which are the values obtained in the original paper.

  • filter_size – The size of the Gaussian filter. Defaults to 11.

  • filter_sigma – The standard deviation of the Gaussian filter. Defaults to 1.5.

  • k1 – Factor used to calculate the regularization constant for the luminance term, as C1 = (k1 * max_val) ** 2. Defaults to 0.01.

  • k2 – Factor used to calculate the regularization constant for the contrast term, as C2 = (k2 * max_val) ** 2. Defaults to 0.03.

  • batch_dims –

    An int. The number of batch dimensions in input images. If None, it is inferred from inputs and image_dims as (rank of inputs) - image_dims - 1. If image_dims is also None, then batch_dims defaults to 1. batch_dims can always be inferred if image_dims was specified, so you only need to provide one of the two.

  • image_dims –

    An int. The number of spatial dimensions in input images. If None, it is inferred from inputs and batch_dims as (rank of inputs) - batch_dims - 1. Defaults to None. image_dims can always be inferred if batch_dims was specified, so you only need to provide one of the two.

  • rank –

    An int. The number of spatial dimensions. Must be 2 or 3. Defaults to tf.rank(y_true) - 2. In other words, if rank is not explicitly set, y_true and y_pred should have shape [batch, height, width, channels] if processing 2D images or [batch, depth, height, width, channels] if processing 3D images.

  • multichannel – A boolean. Whether multichannel computation is enabled. If False, the inputs y_true and y_pred are not expected to have a channel dimension, i.e. they should have shape batch_shape + [height, width] (2D) or batch_shape + [depth, height, width] (3D).

  • complex_part – The part of a complex input to be used in the computation of the metric. Must be one of 'real', 'imag', 'abs' or 'angle'. Note that real and imaginary parts, as well as angles, will be scaled to avoid negative numbers.

  • reduction – Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO.

  • name – String name of the loss instance.

References

1

Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2016). Loss functions for image restoration with neural networks. IEEE Transactions on computational imaging, 3(1), 47-57.

DEPRECATED FUNCTION ARGUMENTS

Deprecated: SOME ARGUMENTS ARE DEPRECATED: (rank). They will be removed after 2022-09-01. Instructions for updating: Use argument image_dims instead.