Non-uniform Deblurring for Shaken Images

• Published in: International journal of computer vision Vol. 98; no. 2; pp. 168 - 186
• Main Authors: Whyte, Oliver, Sivic, Josef, Zisserman, Andrew, Ponce, Jean
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.06.2012; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Applied sciences; Approximation; Artificial Intelligence; Cameras; Computer Imaging; Computer Science; Computer science; control theory; systems; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Image Processing and Computer Vision; Image processing systems; Information, signal and communications theory; Kernels; Mathematical models; Pattern Recognition; Pattern Recognition and Graphics; Pattern recognition. Digital image processing. Computational geometry; Rotational; Shutters; Signal and communications theory; Signal, noise; Social exclusion; Studies; Telecommunications and information theory; Three dimensional; Vision; Vision systems; Motion blur; Blind deconvolution; Non-uniform/spatially-varying blur; Camera shake; Image processing; Noise reduction; blurring image; Mobility; Blurred image; Modeling; Geometrical model; Noisy image; Convolution; Model matching; Efficiency; Marginal distribution; Posterior distribution; Signal to noise ratio
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-011-0502-7

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.