Reconstruct the iceit images using the bfgs variable metric method and obtain better images . 5 将数学中的bfgs变尺度法应用于iceit的成像,改善了成像结果。
Modified constrained variable metric method is adopted in this optimization , and the application of perturbation method in the re - analysis of the structure is studied 另外,还对结构重分析中摄动法的应用进行了讨论。
Modified constrained variable metric method is adopted in this optimization , and the application of perturbation method in the re - analysis of the structure is studied 另外,还对结构重分析中摄动法的应用进行了讨论。
The accuracy of the final results is better than 0 . 5 % after 1000 measurement data averaged . applying the variable metric method , we got preliminary images based on physical phantom 对硬件系统的测量结果表明,对测得的数据经1000次叠加后测量精度优于0 . 5 。
We reconstruct the dynamic images using the data measured from our physical phantom . the bfgs variable metric method is nonsensitive to noise , the result background is even on the whole . the objects in the images are clear 基于物理模型的实测数据的成像结果表明: bfgs变尺度法抗噪性能较好,图像的背景比较均匀,目标清晰;目标图像的大小与实际大小接近,定位较准确。
In the image reconstruction based on optimization without constraint , the variable metric method , the steepest descent method , and conjugate gradient method were applied to image reconstruction to improve iterative efficiency and reconstructed quality , and their virtue and shortcoming were analyzed 摘要在无约束最优化为基础的图像重建问题中,为了提高迭代效率以及重建图像质量,首次提出将变度量法应用到图像重建中。
Such methods are generally decreasing method , such as , feasible direction methods , constrained variable metric methods , etc . another class is sub - problems method , which approximates the optimal solution by solving a series of simple sub - problems , such as penalty function methods , trust region methods , and successive quadratic programming sub - problems , etc . the same property of two classes of methods is that they determine whether the next iterative point is " good " or " bad " by comparing the objective function value or merit function value at the current point and next iterative point 另一类叫做子问题算法,这种算法是通过一系列简单子问题的解来逼近原问题的最优解,如罚函数法、信赖域算法、逐步二次规划算法等。这两类算法的一个共同特点是,通过比较当前点和下一个迭代点的目标函数值或评价函数值来确定迭代点的“优”或“劣” ,若迭代点比当前点“优”则该迭代点可以被接受,否则须继续搜索或调整子问题。