基于非局部全变差模型和全局非零局部秩惩罚的图像去模糊

Image Deblurring Based on Non-Local Total Variation and Global Non-Zero Local Rank Penalty

摘要: 图像成像及分析模块是未来汽车应用系统中的重要组成部分，清晰的图像为后续的智能控制提供可靠保证。然而，由于成像设备自身硬件的问题，使得图像出现模糊等问题。因此，为了能够从降质图像中复原出高质量的清晰图像，并为后续的处理带来便利，本文提出一种基于非局部全变差模型和全局非零局部秩惩罚的图像去模糊方法。非局部全变差模型主要用于恢复图像中的纹理细节，而非零局部秩惩罚则主要用于约束图像的边缘，达到锐化边缘的目的。本文所提出的方法在模拟图像和真实模糊图像的去模糊上都取得了很好的效果。

Abstract: The imaging and analysis module is an important part of automobile application system in the fu-ture, and clear images provide a reliable guarantee for the intelligent control system. However, due to the existing problems of imaging equipment hardware, the obtained images appear blurring. Therefore, in order to restore the clean images from the blur ones and bring convenience to the subsequent processing, this paper proposes an image deblurring method based on non-local total variation and global non-zero local rank penalty. The non-local total variation model is mainly used to restore the texture details of image, and the non-zero local rank penalty is mainly used to sharp the edge of the image. The proposed deblurring method in this paper has achieved better results on simulated images and real blurred image than other methods.