图 书 馆
使用内部补丁递归的盲去模糊Blind Deblurring Using Internal Patch Recurrence |
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| 课程网址: | http://videolectures.net/eccv2014_michaeli_blind_deblurring/ |
| 主讲教师: | Tomer Michaeli |
| 开课单位: | 魏茨曼科学研究所 |
| 开课时间: | 2014-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Recurrence of small image patches across different scales of a natural image has been previously used for solving ill-posed problems (e.g. super- resolution from a single image). In this paper we show how this multi-scale property can also be used for “blind-deblurring”, namely, removal of an unknown blur from a blurry image. While patches repeat ‘as is’ across scales in a sharp natural image, this cross-scale recurrence significantly diminishes in blurry images. We exploit these deviations from ideal patch recurrence as a cue for recovering the underlying (unknown) blur kernel. More specifically, we look for the blur kernel k, such that if its effect is “undone” (if the blurry image is deconvolved with k), the patch similarity across scales of the image will be maximized. We report extensive experimental evaluations, which indicate that our approach compares favorably to state-of-the-art blind deblurring methods, and in particular, is more robust than them. |
| 关 键 词: | 补丁递归; 自然图像; 超分辨率 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-07-19：chenxin01 |
| 最后编审: | 2023-07-19：chenxin01 |
| 阅读次数: | 17 |