Image Deblurring Based on Non-Local Total Variation and Global Non-Zero Local Rank Penalty
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.
文章引用: 汤 捷 , 夏静满 , 刘 荣 , 李星灿 , 厉 伟 (2015) 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。 自然科学， 3， 12-18. doi: 10.12677/OJNS.2015.32003
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