Second-order Cone Programming Methods for Total Variation-Based Image Restoration

Second-order Cone Programming Methods for Total Variation-Based Image Restoration

In this paper we present optimization algorithms for image restoration based on the total variation (TV) minimization framework of Rudin, Osher, and Fatemi (ROF). Our approach formulates TV minimization as a second-order cone program which is then solved by interior-point algorithms that are efficient both in practice (using nested dissection and domain decomposition) and in theory (i.e., they ...

Total Variation Based Methods for Image Restoration

Contacts: Tel: (65)-6516-6250 Office: S14-05-21 Email: [email protected] I mage restoration refers to the process of recovering an ideal image from its corrupted version which is the one we actually observe. The corruptions may be due to noise along the way of acquisition, out-of-focus of the imaging device, or packet loss during transmission of an acquired image from one location to another. ...

Second-order cone programming

Second-order cone programming (SOCP) problems are convex optimization problems in which a linear function is minimized over the intersection of an affine linear manifold with the Cartesian product of second-order (Lorentz) cones. Linear programs, convex quadratic programs and quadratically constrained convex quadratic programs can all be formulated as SOCP problems, as can many other problems t...

Total variation based image denoising and restoration

This paper is devoted to the total variation (TV) based approach to image denoising and restoration. The restored image minimizes total variation in the class of images which satisfy the contraints given by the image acquisition model. We compute some explicit solutions of the denoising model which explain some of the features observed in numerical experiments. We also comment on some alternati...

Iterative Methods for Total Variation Image Restoration 1