OUTLIERS DISPOSING SOLUTION IN CAMERA-SHAKE IMAGE RESTORATION

Abstract

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, 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.  The experimental results show that the method is practical and effective; this method also triggers the thinking about a new approach for blured image restoration.