Improved L0 Gradient Minimization with L1 Fidelity for Image Smoothing

Xueshun Pang; Suqi Zhang; Junhua Gu; Lingling Li; Boying Liu; Huaibin Wang

doi:10.1371/journal.pone.0138682

Improved L0 Gradient Minimization with L1 Fidelity for Image Smoothing

PLoS One. 2015 Sep 18;10(9):e0138682. doi: 10.1371/journal.pone.0138682. eCollection 2015.

Authors

Xueshun Pang¹, Suqi Zhang², Junhua Gu³, Lingling Li³, Boying Liu³, Huaibin Wang⁴

Affiliations

¹ School of Computer Science and Communication Engineering, TJUT, Tianjin, China; School of Information Engineering, Tianjin University of Commerce, Tianjin, China; The Key Lab of Big Data Computing of Hebei Province, School of Computer Science and Engineering, HEBUT, Tianjin, China.
² School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
³ The Key Lab of Big Data Computing of Hebei Province, School of Computer Science and Engineering, HEBUT, Tianjin, China.
⁴ School of Computer Science and Communication Engineering, TJUT, Tianjin, China.

Abstract

Edge-preserving image smoothing is one of the fundamental tasks in the field of computer graphics and computer vision. Recently, L0 gradient minimization (LGM) has been proposed for this purpose. In contrast to the total variation (TV) model which employs the L1 norm of the image gradient, the LGM model adopts the L0 norm and yields much better results for the piecewise constant image. However, as an improvement of the total variation (TV) model, the LGM model also suffers, even more seriously, from the staircasing effect and is not robust to noise. In order to overcome these drawbacks, in this paper, we propose an improvement of the LGM model by prefiltering the image gradient and employing the L1 fidelity. The proposed improved LGM (ILGM) behaves robustly to noise and overcomes the staircasing artifact effectively. Experimental results show that the ILGM is promising as compared with the existing methods.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Image Processing, Computer-Assisted / methods*
Phantoms, Imaging*

Grants and funding

This work is supported by the National Natural Science Foundation of China (http://www.nsfc.gov.cn/) under grant 51475136, the Program from the Tianjin Commission of Technology of China (http://www.tstc.gov.cn/) under grants 12JCYBJC12400, 13JCQNJC00200, and 15JCQNJC00600, and by the training project of HEBUT for newly developed key interdisciplinary. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.