目的 在曝光过程中由于相机抖动而导致的图像模糊，是一种常见的图像降质现象，并且模糊图像中存在的异常值会导致复原结果的振铃效应，为了解决这些问题，本文提出一种处理异常值的相机抖动模糊图像复原算法。方法 该算法以自然图像统计为先验模型，结合变分贝叶斯方法和KL散度（Kullback-Leibler divergence）构造易于优化的代价函数，进而求出模糊核。针对异常值造成的振铃效应，在解卷积的过程中采用期望-最大值算法估计并处理异常值以抑制振铃效应。结果 采用本文方法对大量模糊图片进行复原，实验结果表明该方法能有效地去除相机抖动产生的模糊，在保持图像边缘和细节的同时，可有效抑制振铃效应。结论 提出了一种通过处理异常值达到抑制振铃效应目的，进而提高复原效果的图像盲复原新方法。实验结果表明该方法切实有效，并且该方法引出了一种抑制振铃效应的新思路。

Objective 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. Method 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. Result 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. Conclusion 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.