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非线性抛物型方程中时间向后的危险延续以及非线性模糊图像去模糊的一个实验

Hazardous Continuation Backward in Time in Nonlinear Parabolic Equations, and an Experiment in Deblurring Nonlinearly Blurred Imagery.

作者信息

Carasso Alfred S

机构信息

National Institute of Standards and Technology, Gaithersburg, MD 20899.

出版信息

J Res Natl Inst Stand Technol. 2013 Apr 24;118:199-217. doi: 10.6028/jres.118.010. eCollection 2013.

Abstract

Identifying sources of ground water pollution, and deblurring nanoscale imagery as well as astronomical galaxy images, are two important applications involving numerical computation of parabolic equations backward in time. Surprisingly, very little is known about backward continuation in nonlinear parabolic equations. In this paper, an iterative procedure originating in spectroscopy in the 1930's, is adapted into a useful tool for solving a wide class of 2D nonlinear backward parabolic equations. In addition, previously unsuspected difficulties are uncovered that may preclude useful backward continuation in parabolic equations deviating too strongly from the linear, autonomous, self adjoint, canonical model. This paper explores backward continuation in selected 2D nonlinear equations, by creating fictitious blurred images obtained by using several sharp images as initial data in these equations, and capturing the corresponding solutions at some positive time T. Successful backward continuation from t=T to t = 0, would recover the original sharp image. Visual recognition provides meaningful evaluation of the degree of success or failure in the reconstructed solutions. Instructive examples are developed, illustrating the unexpected influence of certain types of nonlinearities. Visually and statistically indistinguishable blurred images are presented, with vastly different deblurring results. These examples indicate that how an image is nonlinearly blurred is critical, in addition to the amount of blur. The equations studied represent nonlinear generalizations of Brownian motion, and the blurred images may be interpreted as visually expressing the results of novel stochastic processes.

摘要

识别地下水污染的来源、使纳米级图像以及天文星系图像去模糊,是涉及抛物型方程时间反向数值计算的两个重要应用。令人惊讶的是,对于非线性抛物型方程中的反向延拓,人们了解得非常少。在本文中,一种起源于20世纪30年代光谱学的迭代过程,被改编成一种用于求解一大类二维非线性反向抛物型方程的有用工具。此外,还发现了一些以前未被怀疑的困难,这些困难可能会妨碍在偏离线性、自治、自伴、规范模型过强的抛物型方程中进行有用的反向延拓。本文通过在这些方程中使用几个清晰图像作为初始数据来创建虚拟模糊图像,并在某个正时间(T)捕捉相应的解,来探索选定二维非线性方程中的反向延拓。从(t = T)成功反向延拓到(t = 0),将恢复原始的清晰图像。视觉识别为重建解的成功或失败程度提供了有意义的评估。开发了一些有启发性的例子,说明了某些类型非线性的意外影响。给出了视觉上和统计上难以区分的模糊图像,但去模糊结果却大不相同。这些例子表明,除了模糊量之外,图像的非线性模糊方式也至关重要。所研究的方程代表了布朗运动的非线性推广,并且模糊图像可以被解释为直观地表达了新型随机过程的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b78/4487310/9f365d44f655/jres.118.010f1.jpg

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