一种全局优化的SAR图像分割模型可轻松转化为通用ROF去噪模型
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model
AI总结：
提出基于AA去噪模型和变分水平集的局部统计活动轮廓模型用于强度不均匀SAR图像分割，通过凸松弛转化为全局优化模型，并利用分裂Bregman和近端函数转化为ROF去噪模型，实现高效求解。
摘要：
本文提出了一种基于Aubert-Aujol (AA)去噪模型和变分水平集方法的新型局部统计活动轮廓模型（LACM），可用于强度不均匀的SAR图像分割。然后我们通过凸松弛技术将所提模型转化为全局优化模型。首先，我们应用分裂Bregman技术将全局优化模型转化为Shrink算子和Laplace算子的交替优化过程，称为SB_LACM模型。此外，我们提出了两种快速模型来求解全局优化模型，其效率高于SB_LACM模型。第一种模型：我们添加近端函数将全局优化模型转化为通用ROF模型[29]，该模型可通过此http URL提出的快速去噪算法求解；[29]提交于2013年8月29日，我们的早期版本曾于2012年6月12日提交给TGRS，Venkatakrishnan等人[30]于2013年5月29日提出他们的PnP算法，因此Venkatakrishnan和我们几乎同时提出了PnP算法。由此我们获得了一种不涉及偏微分方程或差分方程、仅需简单差分计算的全局优化快速分割算法。第二种模型：我们使用不同于第一种模型的分裂方法，将全局优化模型转化为一个可微项和一个通用ROF模型项，可采用与第一种模型相同的技术求解。
英文摘要：
In this paper, we propose a novel locally statistical active contour model (LACM) based on Aubert-Aujol (AA) denoising model and variational level set method, which can be used for SAR images segmentation with intensity inhomogeneity. Then we transform the proposed model into a global optimization model by using convex relaxation technique. Firstly, we apply the Split Bregman technique to transform the global optimization model into two alternating optimization processes of Shrink operator and Laplace operator, which is called SB_LACM model. Moreover, we propose two fast models to solve the global optimization model , which are more efficient than the SB_LACM model. The first model is: we add the proximal function to transform the global optimization model to a general ROF model[29], which can be solved by a fast denoising algorithm proposed by R.-Q.Jia, and H.Zhao; [29] was submitted on 29-Aug-2013, and our early edition was ever submitted to TGRS on 12-Jun-2012, Venkatakrishnan et al. [30] proposed their PnP algorithm on 29-May-2013, so Venkatakrishnan and we proposed the PnP algorithm almost simultaneously. Thus we obtain a fast segmentation algorithm with global optimization solver that does not involve partial differential equations or difference equation, and only need simple difference computation. The second model is: we use a different splitting approach than one model to transform the global optimization model into a differentiable term and a general ROF model term, which can be solved by the same technique as the first model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
