The patch-based image priors have been successfully applied to blind image deblurring algorithms. But those priors are time-consuming since too many non-linear operators are involved. However, the solving of these prior needs to use nonlinear operators that greatly decrease the computational efficiency. Proposed in this study is a simply patch-wise image prior that uses non-overlapped local patches to compute the local maximal second-order gradient of an image. We find that the values of the patch-wise second-order gradient (PSG) of an image decrease with the motion blur process. A new optimization algorithm is proposed by combining $L_1$ regularized PSG with the maximum posterior probability. Besides, kernel similarity constraint is employed to control the iteration times to reduce the computational costs. Comparative experiments on mainstream datasets show that the results of the presented algorithm are generally better than those of other algorithms on both quantitative contrast and visual contrast.

基于补丁的图像先验已成功应用于盲图像去模糊算法。但是这些先验是耗时的，因为涉及到太多的非线性算子。然而，这些先验的求解需要使用非线性算子，大大降低了计算效率。本研究提出的是一个简单的块状图像先验，它使用非重叠的局部块来计算图像的局部最大二阶梯度。我们发现图像的块状二阶梯度 ( PSG ) 的值随着运动模糊过程而减小。提出了一种新的优化算法${\mathrm{大号}}_1$具有最大后验概率的正则化PSG 。此外，采用核相似性约束来控制迭代次数以降低计算成本。在主流数据集上的对比实验表明，所提出的算法在定量对比和视觉对比上的结果普遍优于其他算法。