Weighting Wiener and total variation for image denoising
文献类型：会议论文
| 作者 | yun liu; bing luo; zhicheng zhang; yanchun zhu; shibin wu; yaoqin xie |
| 出版日期 | 2016 |
| 会议名称 | ICIA 2016 |
| 会议地点 | 中国浙江宁波 |
| 英文摘要 | Image denoising is an integral part in computer vision, pattern recognition and medical image analysis, because images are vulnerable to noises during its acquisition, quantization, compression and transition. Moreover, Image denoising influences its follow-up tasks, such as edge detection, object segmentation, scene understanding and image interpretation. A large number of proposed algorithms have shown pros as well as cons in noise removal and our previous study indicates that it is difficult to remove noise while keep edge structures. To address this problem, this paper explored to combine Wiener and total variation (TV) with an optimal weighting for image quality enhancement, aiming to keep the edge information with Wiener and achieve noise removal with TV. An extensive study shows that at different noise level, corresponding optimal weights can be found to improve denoising performance with well kept edge structures and suppressed noise. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10560]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | yun liu,bing luo,zhicheng zhang,et al. Weighting Wiener and total variation for image denoising[C]. 见:ICIA 2016. 中国浙江宁波. |
入库方式： OAI收割
来源：深圳先进技术研究院
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