Edge preserving image restoration using Augmented Lagrangian method

We propose a new fast algorithm for solving one of the standard approaches to ill-posed linear inverse problems (IPLIP), where a (possibly non-smooth) regularizer is minimized under the constraint that the solution explains the observations sufficiently well. Here we are implementing an Augmented Lagrangian method [4] provides a strategy to handle equality and inequality constraints by introducing the augmented Lagrangian function.