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首页> 外文会议>Conference on Independent Component Analyses, Wavelets, and Neural Networks Apr 22-25, 2003 Orlando, Florida, USA >Blind Inversion in Nonlinear Space-Variant Imaging by Using Cauchy Machine
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Blind Inversion in Nonlinear Space-Variant Imaging by Using Cauchy Machine

机译：用柯西机对非线性空间变量成像进行盲目反演

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摘要

A Cauchy Machine has been applied to solve nonlinear space-variant blind imaging problem with positivity constraints on the pixel-by-pixel basis. Nonlinearity parameters, de-mixing matrix and source vector are found at the minimum of the thermodynamics free energy H=U-T_0S, where U is estimation error energy, T_0 is temperature and S is the entropy. Free energy represents dynamic balance of an open information system with constraints defined by data vector. Solution was found through Lagrange Constraint Neural Network algorithm for computing the unknown source vector, exhaustive search to find unknown nonlinearity parameters and Cauchy Machine for seeking de-mixing matrix at the global minimum of H for each pixel. We demonstrate the algorithm capability to recover images from the synthetic noise free nonlinear mixture of two images. Capability of the Cauchy Machine to find the global minimum of the 'golf hole' type of landscape has hitherto never been demonstrated in higher dimensions with a much less computation complexity than an exhaustive search algorithm.
机译：柯西机已经被用于解决具有逐个像素正约束性的非线性空间变量盲成像问题。在热力学自由能H = U-T_0S的最小值处找到非线性参数，解混合矩阵和源矢量，其中U是估计误差能量，T_0是温度，S是熵。自由能表示开放信息系统的动态平衡，其约束由数据向量定义。通过拉格朗日约束神经网络算法计算未知源矢量，进行穷举搜索以找到未知非线性参数，并使用柯西机器在每个像素的全局最小值H处寻找解混合矩阵，从而找到了解决方案。我们演示了从两个图像的无合成噪声非线性混合中恢复图像的算法功能。迄今为止，柯西机器发现“高尔夫球场”类型景观的全局最小值的能力从未在更高的维度上得到证明，其计算复杂度远低于穷举搜索算法。

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