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Image smoothing via gradient sparsity and surface area minimization
![](https://sigport.org/sites/default/files/styles/home/public/1525-Jun%27s-Poster_0a.jpg?itok=f8kxpLdn)
- Citation Author(s):
- Submitted by:
- Jun Liu
- Last updated:
- 20 September 2019 - 11:32am
- Document Type:
- Poster
- Document Year:
- 2019
- Event:
- Presenters:
- Jun Liu
- Paper Code:
- 1525
- Categories:
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Image smoothing is a very important topic in image processing. Among these image smoothing methods, the $L_0$ gradient minimization method is one of the most popular ones. However, the $L_0$ gradient minimization method suffers from the staircasing effect and over-sharpening issue, which highly degrade the quality of the smoothed image. To overcome these issues, we use not only the $L_0$ gradient term for finding edges, but also a surface area based term for the purpose of smoothing the inside of each region. An alternating minimization algorithm is suggested to efficiently solve the proposed model, where each subproblem has a closed-form solution. Leveraging the introduced surface area term, the proposed method can effectively alleviate the staircasing effect and the over-sharpening issue. The superiority of our method over the state-of-the-art methods is demonstrated by a series of experiments.