针对在实际环境中很难得到图像去模糊所需要的大量先验知识,诸如退化模式、点扩散函数等,提出了一种新的图像去模糊清晰化方法.首先利用高斯差分算子获得图像轮廓信息,然后根据轮廓信息预测清晰图像过渡区,再利用清晰图像过渡区、退化图像和点扩散函数之间的关系建立目标函数,为了克服噪声的影响,在目标函数中加入了非负性惩罚项和空间相关性约束项,并使用滞后迭代的极小化方法来求解点扩散函数;最后通过已有的非盲目图像复原算法复原图像.实验结果表明,该方法无需知道图像退化模式,对各种因素引起的退化图像都能有效地复原.%Due to the various limitations in real restoration process, it is difficult to get the image blur mode or point spread function (PSF). A new method for image deblurring is proposed in this paper. At first, the proposed deblurring method uses the different of Gaussian operator (DoG) to detect the counters of blur image. Then the information of transition region of original image can be predicted according to the contours of blur image. Then the objective function is established according to the original image, transition region, and point spread function. In order to overcome the influence of noise, the nonnegative penalty term and space correlation penalty term with anisotropic features are added in objective function, and the PSF is solved using the minimization method of hysteresis iteration. Finally, the clear image can be gained by the existing the non-blind image restoration methods. Experimental results show that the proposed method can effectively restore the blur images caused by various factors. It does not need to know the image blur model.
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