Improved L0 Gradient Minimization with L1 Fidelity for Image Smoothing.
Xueshun Pang1, Suqi Zhang2, Junhua Gu3
1School 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.
Plos One
|September 19, 2015
Summary
This study introduces an improved L0 gradient minimization (ILGM) method for edge-preserving image smoothing. The ILGM model effectively reduces noise and staircasing artifacts, offering promising results compared to existing techniques.
Area of Science:
- Computer Graphics
- Computer Vision
- Image Processing
Background:
- Edge-preserving image smoothing is crucial in computer graphics and vision.
- L0 gradient minimization (LGM) offers improvements over Total Variation (TV) for piecewise constant images.
- LGM suffers from staircasing effects and noise sensitivity, similar to TV models.
Purpose of the Study:
- To address the limitations of LGM, specifically noise sensitivity and staircasing artifacts.
- To propose an improved LGM (ILGM) model for robust and effective image smoothing.
- To enhance edge-preserving capabilities while mitigating common artifacts.
Main Methods:
- Prefiltering the image gradient to enhance robustness.
- Employing L1 fidelity to improve noise handling.
- Developing an improved L0 gradient minimization (ILGM) algorithm.
Main Results:
- The proposed ILGM model demonstrates robustness to noise.
- ILGM effectively overcomes the staircasing artifact.
- Experimental results indicate ILGM performs promisingly compared to existing methods.
Conclusions:
- The improved LGM (ILGM) model offers a robust solution for edge-preserving image smoothing.
- ILGM effectively mitigates noise and staircasing artifacts, outperforming previous methods.
- The ILGM approach is a valuable advancement in image processing techniques.
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