Progressively Distribution-based Rician Noise Removal for Magnetic Resonance Imaging
文献类型：会议论文
| 作者 | Qiegen Liu; Sanqian Li; Jiujie Lv; Dong Liang |
| 出版日期 | 2018 |
| 会议日期 | 2018年 |
| 会议地点 | 巴黎 |
| 英文摘要 | Different from the existing MRI denoising methods that utilizing the spatial neighbor information around the pixels or patches, this work turns to capture the pixel-level distribution information by means of supervised network learning. A wide and progressive network learning strategy is proposed, via fitting the distribution at pixel-level and feature-level with large convolutional filters. The whole network is trained in a two-stage fashion, consisting of the residual network in pixel domain with batch normalization layer and in feature domain without batch normalization layer. Experiments demonstrate its great potential with substantially improved SNR and preserved edges and structures. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14566]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Qiegen Liu,Sanqian Li,Jiujie Lv,et al. Progressively Distribution-based Rician Noise Removal for Magnetic Resonance Imaging[C]. 见:. 巴黎. 2018年. |
入库方式： OAI收割
来源：深圳先进技术研究院
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