基于多种先验的盲图像复原方法

基于好的还原图像是倾向于清晰图像而不是模糊图像这样一个事实,提出了一种基于多种先验的有效的盲图像去模糊方法。目前比较好的去模糊方法对于特定场景图像的复原效果不理想,存在一些模糊,包括轮廓和细节表示不清晰。为解决这些问题,结合多个先验知识,包括暗通道先验、强度图像先验和梯度图像先验知识,并加以权衡,就可以在复原过程中为轮廓和细节提供更多的先验信息,并把这个先验知识放到MAP的框架中,通过不断地迭代得到估计模糊核,再利用非盲的图像复原方法对原图像复原。在泛化处理自然环境的多种场景中,本文方法相较于目前比较先进的方法,结果的轮廓和细节都有不错的提升。

A blind image deblurring method based on multiple priors

We propose an effective blind image deblurring method based on multiple priors. Our work is motivated by the fact that a good restored image should favor clear images over blurred images. At present, existing deblurring methods are not ideal for image restoration in specific scenes and there are some blurring, including unclear outlines and details. Aiming at these problems, we propose to combine the prior knowledge of multiple priors, including dark channel priors, intensity priors and gradient priors, and to balance them to provide more priori information for outlines and details during the restoration process. This is of great help for blur kernel estimation. We obtain a total prior knowledge by weighing the three priors, and put it into the maximum a posteriori estimation(MAP) framework. The estimated blur kernel is obtained by iterations, and the original image is restored by using the not blind image restoration method. Our results make great progress compared with current advanced methods, especially the outlines and details in various natural scenarios.