Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and regularization terms. At the price of high-computational cost, this global EPS approach is more robust and versatile than a local one that typically has a form of weighted averaging. In this paper, we introduce an efficient decomposition-based method for global EPS that minimizes the objective function of L2 data and (possibly non-smooth and non-convex) regularization terms in linear time. Different from previous decomposition-based methods, which require solving a large linear system, our approach solves an equivalent constrained optimization problem, resulting in a sequence of 1-D sub-problems. This enables applying fast linear time solver for weighted-least squares and - L1 smoothing problems. An alternating direction method of multipliers algorithm is adopted to guarantee fast convergence. Our method is fully parallelizable, and its runtime is even comparable to the state-of-the-art local EPS approaches. We also propose a family of fast majorization-minimization algorithms that minimize an objective with non-convex regularization terms. Experimental results demonstrate the effectiveness and flexibility of our approach in a range of image processing and computational photography applications.
Bibliographical noteFunding Information:
Manuscript received December 1, 2016; revised April 17, 2017; accepted May 23, 2017. Date of publication June 1, 2017; date of current version June 23, 2017. This work was supported in part by the Institute for Information and communications Technology Promotion Grant through the Korea Government (MSIP) under Grant 2016-0-00197 and in part by the National Research Foundation of Korea through the MSIP under Grant 2017R1C1B2005584. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Xudong Jiang. (Corresponding author: Kwanghoon Sohn.) Y. Kim, B. Ham, and K. Sohn are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120–749, South Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design