基于尺度信息边缘提取的模糊核估计方法

模糊图像的盲复原一直以来都是图像处理领域长期的挑战性问题,其中,能否复原出高质量清晰图像的关键是能否准确的估计出引起图像模糊的模糊核(BK)。为了能够实现BK的准确估计,提出了一种基于相对全变差模型(RTVM)的模糊核估计方法。首先,直接将RTVM作为图像的先验,直接代入到最优化的求解过程中,能够在迭代求解的过程中直接复原出锐化的大尺度边缘,而不需要额外的边缘提取步骤;然后,在对BK的正则化约束方面,利用L0范数,在梯度域,对BK的梯度进行L0范数的约束,能够同时保护BK的稀疏特性和连续特性;最后,结合一种分解的策略、迭代的重权重最小平方法(IRLS)和半二次性的变量分裂算法对提出的模型进行最优化求解。为了验证提出方法的优越性,将提出的方法与近几年一些极具代表性的模糊图像盲复原方法在大量的模糊图像上进行了比较实验,实验结果证明了所提方法的优越性。

Scale-based edge extraction method for blur kernel estimation

Blind restoration of the blurred image is a long-standing and challenging problem. And accurate blur kernel( BK) estimation is the key for the success of the blind image deblurring. Therefore,in order to estimate the BK accurately,a relative-total-variation-based BK estimation method is proposed in this paper. First,by making the relative total variation model( RTVM) as the image prior and introducing the RTVM directly into optimization process,the sharp large scale edges can be recovered during the iteration,and without an extra edges extraction step. Then,for the design of the BK regularization constraint term,based on the inherent properties of the BK,a regularization constraint model,which applies the L0 norm into the gradient of the BK,is proposed. The proposed model not only preserves the sparsity,but also preserves the continuity of the BK very well. Finally,the proposed model is solved by combining a decomposed scheme,iterative reweighed least square( IRLS) algorithm and half-quadratic variables splitting algorithm. In order to verify the superiority of the proposed method,extensive experiments are performed on a lot of blurred images,and experimental results indicate that in a comparison with several recent representative blind deblurring methods,the proposed method shows the betterment in terms of both the subjective vision and the objective numerical measurement.