Image Deblurring via Total Variation Based Structured Sparse Model Selection
文献类型：期刊论文
| 作者 | Tieyong zeng; Ma LY(马丽艳) |
| 刊名 | Journal of Scientific Computing
 |
| 出版日期 | 2016-04-01 |
| 文献子类 | 期刊论文 |
| 英文摘要 | In this paper, we study the image deblurring problem based on sparse representation over learned dictionary which leads to promising performance in image restoration in recent years. However, the commonly used overcomplete dictionary is not well structured. This shortcoming makes the approximation be unstable and demand much computational time. To overcome this, the structured sparse model selection （SSMS)over a family of learned orthogonal bases was proposed recently. |
| 源URL | [http://159.226.55.106/handle/172511/16279]  |
| 专题 | 微电子研究所_智能制造电子研发中心 |
| 推荐引用方式 GB/T 7714 | Tieyong zeng,Ma LY. Image Deblurring via Total Variation Based Structured Sparse Model Selection[J]. Journal of Scientific Computing,2016. |
| APA | Tieyong zeng,&马丽艳.(2016).Image Deblurring via Total Variation Based Structured Sparse Model Selection.Journal of Scientific Computing. |
| MLA | Tieyong zeng,et al."Image Deblurring via Total Variation Based Structured Sparse Model Selection".Journal of Scientific Computing (2016). |
入库方式： OAI收割
来源：微电子研究所
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