针对电力视频监控图像中存在的噪声，结合压缩感知理论，采用基于过完备字典的稀疏表示方法进行去噪。使用噪声图像训练过完备字典，其中过完备字典的更新使用K-SVD算法，求解稀疏系数使用OMP算法，且根据算法的特点引入了Dice匹配准则来改进正交匹配追踪算法用于求解稀疏系数，最后重构去噪后的图像。 Matlab仿真实验表明，对添加了不同标准差的高斯噪声的图像，文中方法具有良好的去噪效果，与目前常用的小波函数相比，能更好的降低图像中的高斯白噪声，并且在字典训练过程中直接使用视频拍摄的带噪声图像，即使没有原始的无噪声图像依然能够完成去噪任务。%We address the image de-nosing problem in the video surveillance system in smart grid , the approach is based on compressed sensing and sparse representation theory over over -complete dictionary .We train dictionary by noisy image , and use K-SVD algorithm to update dictionary and OMP to compute sparse representation coefficients . An improved orthogonal matching pursuit algorithm based on atomic matching criterion of Dice coefficient is used to re -construct images.Finally, we can get the de-noised image.The Matlab simulation experiments show that this method is an effective de-noising algorithm , and the de-noising result for Gaussian white noise is better than the wavelet func-tions.Using the video images which is corrupted to train the dictionary , while the de-noising task could be completed efficiently even there is no high-quality original image .
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