非均匀的去模糊图像动摇Non-uniform Deblurring for Shaken Images |
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课程网址: | http://videolectures.net/cvpr2010_whyte_ndsp/ |
主讲教师: | Oliver Whyte,; WILLOW; INRIA |
开课单位: | 法国国家信息与自动化研究所 |
开课时间: | 2010-07-19 |
课程语种: | 英语 |
中文简介: | 来自相机抖动的模糊主要是由于相机的3D旋转,导致模糊内核在图像上显着不均匀。然而,大多数当前的去模糊方法将观察到的图像建模为具有均匀模糊核的清晰图像的卷积。我们根据曝光期间相机的旋转速度提出了模糊过程的新参数化几何模型。我们将此模型应用于两种不同的相机抖动去除算法:第一种使用单个模糊图像(盲目去模糊),而第二种使用模糊图像和同一场景的清晰但嘈杂的图像。我们证明了我们的方法可以模拟和去除比以前的方法更广泛的模糊,包括作为特殊情况的均匀模糊,并通过对真实图像的实验证明其有效性。 |
课程简介: | Blur from camera shake is mostly due to the 3D rotation of the camera, resulting in a blur kernel that can be significantly non-uniform across the image. However, most current deblurring methods model the observed image as a convolution of a sharp image with a uniform blur kernel. We propose a new parametrized geometric model of the blurring process in terms of the rotational velocity of the camera during exposure. We apply this model to two different algorithms for camera shake removal: the first one uses a single blurry image (blind deblurring), while the second one uses both a blurry image and a sharp but noisy image of the same scene. We show that our approach makes it possible to model and remove a wider class of blurs than previous approaches, including uniform blur as a special case, and demonstrate its effectiveness with experiments on real images. |
关 键 词: | 盲去模糊; 3D旋转; 模糊核 |
课程来源: | 视频讲座网 |
最后编审: | 2021-01-15:yumf |
阅读次数: | 100 |