结构稀疏通道先验盲图像去模糊方法

Blind image deblurring method with structural sparse channel prior

针对盲图像去模糊过程中主结构不准确和边缘不清晰问题,提出了一种结构稀疏通道先验(SSCP)盲图像去模糊方法。SSCP表示模糊图像比清晰图像具有更少结构稀疏通道的先验方法。利用SSCP的性能特性,将其作为新的正则化项引入经典去模糊模型,构建盲去模糊新模型,实现对模糊核的准确估计。通过坐标下降法,交替优化求解潜像与模糊核变量。最后,通过反卷积得到去模糊的清晰复原图像,在基准数据集和自然状态模糊图像上开展主观和客观对比实验,并进行人脸和低亮度真实模糊图像的应用拓展实验。实验结果表明,提出的方法在模糊去除、结构恢复、边缘保留和视觉效果方面的性能比经典去模糊方法平均提高了1.72%,通过该方法设计出的计算装置能够实现机器视觉领域中模糊图像的高精度清晰化处理。

A structure sparse channel prior(SSCP)blind image deblurring approach is presented to address the issues of inaccurate major structures and unclear edges in the blind image deblurring process.A prior method of SSCP shows that blurred images have less structured sparse channels than sharp images.Using the performance features of SSCP,it is introduced as a new regularization term into the standard deblurring model,and a novel blind deblurring model is created to achieve accurate estimation of the blur kernel.Through the coordinate descent approach alternately optimizes the latent image and blurry kernel variables.Finally,deconvolution is used to obtain deblurred clear restored images,subjective and objective comparison experiments on benchmark datasets and natural state blurred images,and application expansion experiments on human faces and low-brightness real blurred images.The experimental results show that the proposed method outperforms the classical deblurring method in terms of blur removal,structure restoration,edge retention,and visual effect by an average of 1.72%,and the computing device designed by this method can achieve a high-precision clarity to process blurred images in the field of machine vision.