Yang Zhenzhen, Yang Zhen, Gui Guan
National Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing 210003, China.
Sensors (Basel). 2018 Apr 12;18(4):1175. doi: 10.3390/s18041175.
Blurred image restoration poses a great challenge under the non-Gaussian noise environments in various communication systems. In order to restore images from blur and alpha-stable noise while also preserving their edges, this paper proposes a variational method to restore the blurred images with alpha-stable noises based on the property of the meridian distribution and the total variation (TV). Since the variational model is non-convex, it cannot guarantee a global optimal solution. To overcome this drawback, we also incorporate an additional penalty term into the deblurring and denoising model and propose a strictly convex variational method. Due to the convexity of our model, the primal-dual algorithm is adopted to solve this convex variational problem. Our simulation results validate the proposed method.
在各种通信系统的非高斯噪声环境下，模糊图像恢复面临着巨大挑战。为了从模糊和α稳定噪声中恢复图像，同时保留其边缘，本文基于子午线分布特性和全变分（TV）提出了一种变分方法来恢复具有α稳定噪声的模糊图像。由于变分模型是非凸的，它不能保证全局最优解。为克服这一缺点，我们还在去模糊和去噪模型中加入了一个额外的惩罚项，并提出了一种严格凸变分方法。由于我们模型的凸性，采用原始对偶算法来求解这个凸变分问题。我们的仿真结果验证了所提方法的有效性。
