In this study, we propose a patch-wise maximum gradient (PMG) prior for effective blind image deblurring. Our work is motivated by the fact that the maximum gradient values of non-overlapping local patches are significantly diminished by blurring; we demonstrate this inherent property both theoretically and using real data. Based on this, we propose a blur kernel estimation model using an ${\mathit{L}}_0$-regularized PMG prior and ${\mathit{L}}_0$-regularized gradient prior. Compared with previous image priors, our PMG prior exhibits a stronger ability to distinguish between clear and blurred images. It also has a deeper sparseness, which significantly reduces the computational cost. To solve the proposed PMG and ${\mathit{L}}_0$-regularized gradient terms, we design an efficient optimization algorithm by introducing a linear operator and improving the iteration strategy. Visual and quantitative experimental results show that our method can achieve excellent performance and is superior to state-of-the-art methods in terms of computational efficiency and recovery quality in various specific scenarios such as natural, face, saturated, and text images.

在这项研究中，我们提出了有效的盲图去模糊之前的逐块最大梯度（PMG）。我们的工作是基于这样一个事实，即不重叠的局部色块的最大梯度值会由于模糊而大大减小；我们将在理论上和使用实际数据来证明这种内在属性。基于此，我们提出了一种模糊核估计模型，该模型使用${\mathit{大号}}_0$-规范化的PMG之前和${\mathit{大号}}_0$-正则化梯度优先。与以前的图像先验相比，我们的PMG先验具有更强的区分清晰图像和模糊图像的能力。它还具有更深的稀疏性，从而显着降低了计算成本。解决拟议的PMG和${\mathit{大号}}_0$-正则化梯度项，我们通过引入线性算子并改进迭代策略来设计一种有效的优化算法。视觉和定量实验结果表明，在自然，人脸，饱和和文本图像等各种特定情况下，我们的方法都可以实现出色的性能，并且在计算效率和恢复质量方面优于最新方法。