Motion blur due to camera shaking during exposure is a common phenomena of image degradation. Moreover

neglecting the outliers that exist in the blurred image will result in the ringing effect of restored images. In order to solve these problems

a method for camera-shake blurred images restoration with disposing of outliers is proposed. The algorithm

which takes the natural image statistics as prior model

combines variational Bayesian estimation theory with Kullback-Leibler divergence to construct a cost function

which can be easily optimized to estimate the blur kernel. Taking into consideration the ringing effect causing by outliers

an expectation-maximization based algorithm for deconvolution is proposed to reduce the ringing effect. A large quantity of blurred images are restored with this algorithm

the experimental results show that the algorithm of blind image restoration can effectively remove the blur caused by camera shaking

and can effectively reduce the ringing effect

while preserving the image edge and details. We propose a new method for blind image restoration

which deals with the outliers for suppressing ringing effect to improve the effect of restoration. The experimental results show that the method is practical and effective; this method also triggers the thinking about a new way to suppress ringing effect.