SIRTIteration

class deepinv.optim.optim_iterators.SIRTIteration(**kwargs)

Bases: OptimIterator

Iterator for the Simultaneous Iterative Reconstruction Technique (SIRT) algorithm.

Class for a single iteration of the SIRT algorithm.

The iteration is given by:

\[\begin{equation*} x_{k+1} = x_k + \tau V A^{\top} W (y - A x_k) \end{equation*}\]

where

• \(\tau\) is a stepsize parameter which should satisfy \(0 < \tau < 2\)

• \(W = \mathrm{diag}\left(\frac{1}{\sum_{i}a_{ij}}\right)\), a diagonal matrix where each element is the inverse of the row sums of \(A\),

• \(V = \mathrm{diag}\left(\frac{1}{\sum_{j}a_{ij}}\right)\), a diagonal matrix where each element is the inverse of the column sums of \(A\).

forward(X, cur_data_fidelity, cur_prior, cur_params, y, physics, **kwargs)

Computes a single iteration of the SIRT algorithm.

:param : param dict X: Dictionary containing the current iterate and the estimated cost. :param : param deepinv.optim.DataFidelity cur_data_fidelity: Instance of the DataFidelity class defining the current data_fidelity. :param : param deepinv.optim.Prior cur_prior: Instance of the Prior class defining the current prior. :param : param dict cur_params: Dictionary containing the current parameters of the algorithm. :param : param torch.Tensor y: Input data. :param : param deepinv.physics.Physics physics: Instance of the physics modeling the observation. :param : return: Dictionary {"est": (x,), "cost": F} containing the updated current iterate and the estimated current cost.