中文摘要：

目的 针对现行图像去模糊消除机制忽略了图像空间结构特征,降低了模糊消除效果,且算法稳定性不佳,无法克服解模糊等的不足,提出了贝叶斯模型耦合非平稳先验的图像去模糊机制。方法基于二阶统计量方法,定义模糊函数;引入滤波因子和超参数,构造非平稳边缘保持先验模型;基于贝叶斯推理,引入雅克比矩阵设计了超参数动态更新机制;用耦合先验模型与贝叶斯模型完成图像复原。在仿真平台上测试了算法的性能。结果 与其他几种机制相比,提出的算法机制去模糊质量更好,局部放大后纹理细节仍然清晰,并且去模糊前后图像的结构相似度更高。结论 提出的算法具有较佳的图像去模糊效果,重构质量理想。

英文摘要：

Objective The current image deblurring removal mechanism ignores the image space structure characteristics, which reduces the deblurring effect, and there are other problems such as poor stability of these algorithms, which cannot overcome the lack of ambiguity. Targeting at these problems, we proposed a Bayesian model coupled non-stationary priors image deblurring mechanism. Methods Based on second-order statistics, vague function was defined. Filtering factor and ultra-parameter structure were introduced to maintain a priors model of non-stationary edge preserving. Based on Bayesian inference, Jacobian matrix was introduced to design the hyperparameter dynamic update mechanism. The coupled priors model and Bayesian model were used to complete image reconstruction. The performance of the algorithm was tested on the simulation platform. Results Compared with several other mechanisms, the mechanism proposed in this paper showed better deblurring performance. The texture details remained clear after local amplification, and the structural similarity of the images before and after deblurring was higher. Conclusion The proposed algorithm had relatively good image deblurring performance and the reconstruction quality was ideal.