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On the computational model of a kind of deconvolution problem

机译:一类反卷积问题的计算模型

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It is known that discretization of a continuous deconvolution problem can alleviate the ill-posedness of the problem. The currently used circulant matrix model, however, does not play such a role. Moreover, the approximation of deconvolution problems by circulant matrix model is rational only if the size of the kernel function is very small. We propose an aperiodic model of deconvolution. For discrete and finite deconvolution problems the new model is an exact one. In the general case, the new model can lead to a nonsingular system of equations that has a lower condition number than the circulant one, and the related computations in the deconvolution can be done efficiently by means of the DFT technique, as in the ease for circulant matrices. The rationality of the new model holds without regard to the size of the kernel and the image. The use of the aperiodic model is illustrated by gradient-based algorithms.
机译:众所周知,连续解卷积问题的离散化可以减轻问题的不适定性。但是,当前使用的循环矩阵模型不起作用。此外,仅当核函数的大小非常小时,通过循环矩阵模型对反卷积问题的逼近才是合理的。我们提出了反卷积的非周期性模型。对于离散和有限反卷积问题,新模型就是一个精确模型。在一般情况下,新模型可能会导致条件数小于循环数的非奇异方程组,并且通过DFT技术可以高效地完成反卷积中的相关计算,这很容易循环矩阵。新模型的合理性不考虑内核和图像的大小。通过基于梯度的算法说明了非周期性模型的使用。

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