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《Journal of Tianjin University of Technology》 2016-01
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Improved L_0 gradient minimization for image smoothing

PANG Xue-shun;WANG Huai-bin;School of Computer and Communication Engineering,Tianjin University of Technology;  
Edge-preserving image smoothing is one of the fundamental tasks in the field of computer vision and computer graphics and the L0 gradient minimization(LGM)method has been proposed for this purpose very recently. As an improvement of the total variation(TV)model,the LGM model adopts L0 norm and yields much better results for the piecewise constant image. However,like as the TV model,the LGM model also suffers 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 staircases effectively. Experimental results show that the ILGM is promising as compared with the existing methods.
【Fund】: 天津市科技支撑计划重点项目(14ZCZDGX00044)
【CateGory Index】: TP391.41
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