Iterated graph Laplacian for image restoration problems

We study the graph Laplacian operator as a regularizer in a generalized Tikhonov framework for linear ill-posed problems.

The Laplacian is updated iteratively from the current reconstruction, so that progressively sharper structural information about the solution is fed into the regularization term.

We introduce three schemes: a standard one that rebuilds the Laplacian from each new iterate; an error-equation scheme that, following the error-based formulation of iterated Tikhonov regularization, builds the Laplacian from an estimate of the reconstruction error rather than of the image itself; and a mixed scheme combining the two.

We establish convergence of all three schemes for noisy data under a priori parameter and stopping rules as the noise level tends to zero.

Numerical experiments in two-dimensional computed tomography and image deblurring show consistent gains in reconstruction quality and sharper recovery of fine details.