Image denoising via nonlocally sparse coding and tensor decomposition
文献类型：会议论文
| 作者 | Wenrui Hu ; Yuan Xie; Wensheng Zhang; Limin Zhu; Yanyun Qu; Yuanhua Tan
|
| 出版日期 | 2014 |
| 会议日期 | July 10, 2014 - July 12, 2014 |
| 会议地点 | Xiamen, China |
| 关键词 | Collaborative Filtering Sparse Coding Ttensor Decomposition Bregman Iteration |
| 英文摘要 | The nonlocally sparse coding and collaborative filtering techniques have been proved very effective in image denoising, which yielded state-of-the-art performance at this time. In this paper, the two approaches are adaptively embedded into a Bayesian framework to perform denoising based on split Bregman iteration. In the proposed framework, a noise-free structure part of the latent image and a refined observation with less noise than the original observation are mixed as constraints to finely remove noise iteration by iteration. To reconstruct the structure part, we utilize the sparse coding method based on the proposed nonlocally orthogonal matching pursuit algorithm (NLOMP), which can improve the robustness and accuracy of sparse coding in present of noise. To get the refined observation, the collaborative filtering method are used based on Tucker tensor decomposition, which can takes full advantage of the multilinear data analysis. Experiments illustrate that the proposed denoising algorithm achieves highly competitive performance to the leading algorithms such as BM3D and NCSR. |
| 会议录 | ACM International Conference Proceeding Series, 2014:283-288
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12261]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Wensheng Zhang |
| 推荐引用方式 GB/T 7714 | Wenrui Hu,Yuan Xie,Wensheng Zhang,et al. Image denoising via nonlocally sparse coding and tensor decomposition[C]. 见:. Xiamen, China. July 10, 2014 - July 12, 2014. |
入库方式： OAI收割
来源：自动化研究所
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