title: Efficient minimization method for a generalized total variation functional.
abstract: Replacing the l(2) data fidelity term of the standard Total Variation (TV) functional with an l(1) data fidelity term has been found to offer a number of theoretical and practical benefits. Efficient algorithms for minimizing this l(1)-TV functional have only recently begun to be developed, the fastest of which exploit graph representations, and are restricted to the denoising problem. We describe an alternative approach that minimizes a generalized TV functional, including both l(2)-TV and l(1)-TV as special cases, and is capable of solving more general inverse problems than denoising (e.g., deconvolution). This algorithm is competitive with the graph-based methods in the denoising case, and is the fastest algorithm of which we are aware for general inverse problems involving a nontrivial forward linear operator.
authors.affiliation: Digital Signal Processing Group, Pontificia Universidad Católica del Perú, Lima, Peru. prodrig@pucp.edu.pe
authors.forename: Paul
authors.initials: P
authors.lastname: Rodríguez
authors.forename: Brendt
authors.initials: B
authors.lastname: Wohlberg
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity