基于先验优化的一致性模糊盲复原算法

为了提高一致性模糊图像盲复原清晰度，针对复原过程中涉及的全变差模型先验约束问题，提出一种基于先验优化的一致性模糊盲复原算法.利用基于半高斯梯度算子的局部加权全变差模型提取模糊图像显著边缘，在去除噪声和纹理干扰的同时，可提高有利信息的保持能力；提出多尺度混合特性先验估计模糊核，增强了模糊核估计的准确性；利用非盲去卷积得到了清晰的复原图像.实验结果表明，相较其他算法，针对模拟模糊图像，所提算法的复原图像峰值信噪比平均提升约1.7%，结构相似性指数平均提升约19.1%；针对真实模糊图像，复原图像伪影更少，边缘纹理细节更加清晰自然，整体视觉效果更好.

Consistent Blur Blind Restoration Algorithm Based on Prior Optimization

In order to improve the clarity of the blind restoration of the conformance fuzzy image, a prior fuzzy blind restoration algorithm based on the prior optimization is proposed for the study of the prior constraint problem of the full variational model involved in the restoration process. Firstly, the local weighted total variation model based on half Gauss gradient operator is used to extract the significant edge of the blurred image. The noise and texture interference are removed, and the ability to maintain the favorable information is improved. Then a multi-scale mixed characteristic prior estimation of blur kernel is proposed to enhance the accuracy of blur kernel estimation. Finally, clear restored images are obtained by non-blind deconvolution. The experimental results show that compared with other algorithms, the proposed algorithm improves the average peak signal to noise ratio of the reconstructed image by about 1.7%, and the average structure similarity index increases by about 19.1%. In view of the real blur image, the artifact of restored image is less, the edge texture details are more clear and natural, and the overall visual effect is better.