Non-uniform Deblurring for Shaken Images

被引:419
作者
Whyte, Oliver [1 ,4 ]
Sivic, Josef [1 ,4 ]
Zisserman, Andrew [2 ,4 ]
Ponce, Jean [3 ,4 ]
机构
[1] INRIA, Paris, France
[2] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England
[3] Ecole Normale Super, Dept Informat, F-75231 Paris, France
[4] Ecole Normale Super, Willow Project, Lab Informat, CNRS ENS INRIA UMR 8548, Paris, France
关键词
Motion blur; Blind deconvolution; Camera shake; Non-uniform/spatially-varying blur;
D O I
10.1007/s11263-011-0502-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Photographs taken in low-light conditions are often blurry as a result of camera shake, i.e. a motion of the camera while its shutter is open. Most existing deblurring methods model the observed blurry image as the convolution of a sharp image with a uniform blur kernel. However, we show that blur from camera shake is in general mostly due to the 3D rotation of the camera, resulting in a blur that can be significantly non-uniform across the image. We propose a new parametrized geometric model of the blurring process in terms of the rotational motion of the camera during exposure. This model is able to capture non-uniform blur in an image due to camera shake using a single global descriptor, and can be substituted into existing deblurring algorithms with only small modifications. To demonstrate its effectiveness, we apply this model to two deblurring problems; first, the case where a single blurry image is available, for which we examine both an approximate marginalization approach and a maximum a posteriori approach, and second, the case where a sharp but noisy image of the scene is available in addition to the blurry image. 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 synthetic and real images.
引用
收藏
页码:168 / 186
页数:19
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