L1
- class colibri.recovery.terms.fidelity.L1[source]
Bases:
ModuleL1 fidelity
\[f(\mathbf{x}) = ||\forwardLinear(\mathbf{x}) - \mathbf{y}||_1\]- forward(x, y, H)[source]
Computes the L1 fidelity term.
- Parameters:
x (torch.Tensor) – The image to be reconstructed.
y (torch.Tensor) – The measurement data to be reconstructed.
H (function) – The forward model.
- Returns:
The L1 fidelity term.
- Return type:
torch.Tensor
- grad(x, y, H)[source]
Compute the gradient of the L1 fidelity term.
\[\nabla f(\mathbf{x}) = \nabla \frac{1}{2}||\forwardLinear(\mathbf{x}) - \mathbf{y}||_1\]- Parameters:
x (torch.Tensor) – Input tensor.
y (torch.Tensor) – The measurement data to be reconstructed.
H (function) – Forward model.
- Returns:
Gradient of the L1 fidelity term.
- Return type:
torch.Tensor