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New selection methods of regularization parameter for electrical resistance tomography image reconstruction

机译:电阻层析成像图像重建正则化参数的新选择方法

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Image reconstruction in Electrical Resistance Tomography (ERT) is a nonlinear and ill-posed inverse problem. The standard Tikhonov regularization method is always used to solve the ERT problem. However, in Tikhonov method, to guarantee the optimization problem have a solution, usually the regularization parameter matrix always is composed of diagonal matrix, and all the diagonal elements in the matrix are equal to each other. Under this condition, much useful information must be lost. In this paper, two new selection methods of regularization parameter are proposed, which could make the most of sensitivity coefficient matrix. The new methods use l1 norm and l2 norm of sensitivity coefficient matrix respectively to reconstitute regularization parameter. Experiments result proved the two methods could obtain better space resolution than the standard Tikhonov regularization optimization method.
机译:电阻断层扫描(ERT)中的图像重建是一个非线性和不良逆问题。标准的Tikhonov正规方法始终用于解决ERT问题。然而,在Tikhonov方法中,为了保证优化问题具有解决方案,通常正数参数矩阵始终由对角线矩阵组成,并且矩阵中的所有对角线元素彼此等于。在这种情况下,必须丢失很多有用的信息。在本文中,提出了两种新的正则化参数选择方法,这可以充分发挥最大的灵敏度系数矩阵。新方法分别使用灵敏度系数矩阵的L1规范和L2规范重构正则化参数。实验结果证明了这两种方法可以获得比标准的Tikhonov正则化优化方法更好的空间分辨率。

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