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计算机系统应用:2018,27(11):192-197
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含有L1数据保真项的非凸优化脉冲噪声去除模型
陈静思1, 李春2,3
(1.云南财经大学 云南省经济社会大数据研究院, 昆明 650221;2.中国科学院大学, 北京 100049;3.中国科学院 计算机网络信息中心, 北京 100190)
Non-Convex Optimized Impulse Noise Removal Model with L1 Data Fidelity Term
CHEN Jing-Si1, LI Chun2,3
(1.Big Data Research Institute of Yunnan Economy and Society, Yunnan University of Finance and Economics, Kunming 650221, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China)
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本文已被:浏览 40次   下载 87
投稿时间:2018-04-03    修订日期:2018-04-24
中文摘要: 随着数字图像处理技术的高速发展,图像恢复被广泛应用于医学领域、军事领域、公共防卫领域及农业气象领域.本文综合TVL1、ROF、STVL1(Squares TVL1)、SHI模型,提出了非凸非光滑关于脉冲噪声去除模型,并使用变量分离技术的ADMM算法对模型进行求解,通常情况下,基于梯度的方法不适合非光滑优化,半二次(half-quadratic)和重权最小二乘算法(IRLS)在零点不可微分情况下不能应用到非光滑函数上,Graduated NonConvexity (GNC) algorithms跟踪非光滑和非凸的最小值沿着一系列近似的非光滑能量函数的势能,需要考虑其计算时间.为了处理模型的非凸非光滑项,本文应用多阶凸松弛方法对模型的子问题进行求解,虽然该方法仅导致原始非凸问题的局部最优解,但该局部解是对初始凸松弛的全局解的改进.此外,因为每个阶段都是凸优化问题,所以该方法在计算上是高效的.利用遗传算法对模型参数进行选择,通过在不同图片及不同噪声上的大量实验表明,该模型的鲁棒性、运行时间和ISNR、PSNR都优于其他三个模型.并且该模型能够保持图像的局部信息具有更好的可视化质量.
Abstract:With the rapid development of digital image processing technology, image recovery has been widely used in the fields of medicine, military, public defense, and agro-meteorology. This study integrates TVL1, ROF, Squares TVL1 (STVL1), and SHI model, proposes a non-convex and non-smooth model for removing impulse noise, and uses a variable separation technique ADMM to solve the model. In general, gradient-based methods are not suitable for non-smooth optimizations. Half-quadratic and Iterative Reweighted Least Squares (IRLS) algorithms cannot be applied to non-smooth functions when the zero point is non-differentiable. For non-convex non-smooth terms, Graduated NonConvexity (GNC) algorithms track non-smooth and non-convex minimums along the potential energy of a series of approximate non-smooth energy functions and need to consider their computational time. So in order to deal with non-convex non-smooth terms of the model, the multi-step convex relaxation method is used to solve the subproblem of the model. Although this method only leads to the local optimal solution of the original nonconvex problem, the local solution is an improvement over the global solution of the initial convex relaxation. In addition, because each stage is a convex optimization problem, this method is computationally efficient. The genetic algorithm was used to select the parameters of the model. Through a large number of experiments on different pictures and different noises, the robustness, running time, ISNR and PSNR of the model were better than the other three models. And this model can maintain the local information of the image with better visual quality.
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陈静思,李春.含有L1数据保真项的非凸优化脉冲噪声去除模型.计算机系统应用,2018,27(11):192-197
CHEN Jing-Si,LI Chun.Non-Convex Optimized Impulse Noise Removal Model with L1 Data Fidelity Term.COMPUTER SYSTEMS APPLICATIONS,2018,27(11):192-197

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