ReducedResolutionLoss

class deepinv.loss.ReducedResolutionLoss(metric=None)

Bases: SupLoss

Reduced resolution loss for blur and downsampling problems.

The reduced resolution loss is defined as

\[\frac{1}{n}\|y-\inverse{\forw{y}}\|^2\]

where \(\forw{y}\) is the reduced resolution measurement via further degrading, and the measurement \(y\) is used a supervisory signal.

Warning

This loss can only be used with physics that can be used to meaningfully further degrade the measurements \(y\), such as blur or downsampling. The physics must be defined without an img_size so it can be applied to the measurements \(y\).

Hint

During training, consider using the disable_train_metrics option in deepinv.Trainer to prevent a shape mismatch during metric computation since the reduced resolution output will smaller than ground truth.

This loss was used in Shocher et al.[1] for downsampling tasks, and is named Wald’s protocol [2] for pan-sharpening tasks.

Parameters:

metric (Metric, torch.nn.Module) – metric used for computing data consistency, which is set as the mean squared error by default.

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References:

[1]

Assaf Shocher, Nadav Cohen, and Michal Irani. “zero-shot” super-resolution using deep internal learning. 2017. doi:10.48550/arXiv.1712.06087.

[2]

L. Wald, T. Ranchin, and Marc Mangolini. Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing, 1997.

forward(x_net, y, *args, **kwargs)

Computes the reduced resolution loss.

Parameters:

• x_net (torch.Tensor) – reconstructions.

• y (torch.Tensor) – Measurements.

• physics (deepinv.physics.Physics) – Forward operator associated with the measurements.

• model (torch.nn.Module) – Reconstruction function.

Returns:

(torch.Tensor) loss.