Graph Laplacian for image deblurring

TL;DR

This paper introduces a novel image deblurring method using a graph Laplacian as a regularizer within a variational framework, solved efficiently with ADMM, showing promising results.

Contribution

The paper proposes a new regularization approach for image deblurring using graph Laplacian constructed from the observed image, solved with ADMM.

Findings

Effective deblurring demonstrated on numerical examples

Graph Laplacian regularization improves restoration quality

Method efficiently handles noisy and blurred images

Abstract

Image deblurring is relevant in many fields of science and engineering. To solve this problem, many different approaches have been proposed and among the various methods, variational ones are extremely popular. These approaches are characterized by substituting the original problem with a minimization one where the functional is composed of two terms, a data fidelity term and a regularization term. In this paper we propose, in the classical $\ell^2-\ell^1$ minimization with the non-negativity constraint of the solution, the use of the graph Laplacian as regularization operator. Firstly, we describe how to construct the graph Laplacian from the observed noisy and blurred image. Once the graph Laplacian has been built, we solve efficiently the proposed minimization problem splitting the convolution operator and the graph Laplacian by the alternating direction method of multipliers (ADMM). Some selected numerical examples show the good performances of the proposed algorithm.