我们提出一种新方法用于单幅图像的空间变化模糊识别,在图像中的每个局部小块，这个局部模糊被选择在一个候选PSFS有限集中间，用一个极大似然方法。我们打算用广义似然减少参数的数量，用广义奇异值分解限制计算值，但是要对图像做出适当的边界假设。-We present a new approach for spatially varying blur identification

using a single image. Within each local patch in the

image, the local blur is selected between a finite set of candidate

PSFs by a maximum likelihood approach. We propose

to work with a Generalized Likelihood to reduce the number

of parameters and we use the Generalized Singular Value Decomposition

to limit the computing cost, while making proper

image boundary hypotheses. The resulting method is fast and

demonstrates good performance on simulated and real examples

originating from applications such as motion blur identification

and depth from defocus.