@inproceedings{1c10ff48f31c451095ddfcdc248da245,
title = "Image Laplace Denoising Based on Sparse Representation",
abstract = "Image denoising using sparse and redundant representation has drawn a lot of research attentions. For the existing denoising algorithms, the additive noise is always assumed to follow the Gaussian distribution. But in many application cases, the noise is not Gaussian. In this paper, we address the image Lap lace denoising problem, where the additive noise is Lap lace. Thus, our model is proposed by adopting the Bayesian MAP estimation theory. We operate this model on image patches and show how to solve it with linear programming. Our experimental results have shown good performance of our new method both in terms of peak signal-to-noise ratio (PSNR) and visually.",
keywords = "image denoising, Laplace noise, linear programming, sparse representation",
author = "Jingsha Lv and Fuxiang Wang",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 7th International Conference on Computational Intelligence and Communication Networks, CICN 2015 ; Conference date: 12-12-2015 Through 14-12-2015",
year = "2016",
month = aug,
day = "16",
doi = "10.1109/CICN.2015.80",
language = "英语",
series = "Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "373--377",
booktitle = "Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015",
address = "美国",
}