Fast Frame-Based Image Deconvolution Using Variable Splitting and Constrained Optimization

Abstract

We propose a new fast algorithm for solving one of the standard formulations of frame-based image deconvolution: an unconstrained optimization problem, involving an ${\ell }_2$ data-fidelity term and a non-smooth regularizer. Our approach is based on using variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The resulting algorithm efficiently uses a regularized version of the Hessian of the data fidelity term, thus exploits second order information. Experiments on a set of image deblurring benchmark problems show that our algorithm is clearly faster than previous state-of-the-art methods.

Submitted to the 2009 IEEE Workshop on Statistical Signal Processing

Cited by in corpus (5)

• Fast Image Recovery Using Variable Splitting and Constrained Optimization
• An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
• A Fast Algorithm for the Constrained Formulation of Compressive Image Reconstruction and Other Linear Inverse Problems
• Deconvolution and Restoration of Optical Endomicroscopy Images
• LSALSA: Accelerated Source Separation via Learned Sparse Coding