Fundamental Limits And Degrees Of Freedom Of Imaging Systems
暂无分享,去创建一个
A space-variant or invariant point spread function of an imaging system is used to generate the grammian matrix from a continuous-discrete imaging model. If in) is a point in the continuous aperture of of the object plane and the i-th sample of the image array is given by gi = Is hi( )f(, )ddri (1) then the grammian matrix of the point spread function becomes [V] = s Toht(, (2a) where v. = ST h.(, )h..( (2b) The properties of [V] of interest are the singular values which define both the entropy and degrees of freedom of the imaging system. Using physical constraints on the point spread function, i. e. 0 < h. ( ) 1 (3a) N2 E) = 1 (3b) i= 1 1 we develop a set of experimental curves relating entropy and degrees of freedom. In addition a variable sampling procedure is hypothesized to increase the degrees of freedom for a fixed number of samples (Nx N= N2) to improve the data acquisition and processing efficiency and approach the fundamental limitations of the imaging system.
[1] A. Walther. Gabor’s Theorem and Energy Transfer through Lenses , 1967 .
[2] David Mautner Himmelblau,et al. Applied Nonlinear Programming , 1972 .
[3] Anthony V. Fiacco,et al. Nonlinear programming;: Sequential unconstrained minimization techniques , 1968 .
[4] M. J. Box. A Comparison of Several Current Optimization Methods, and the use of Transformations in Constrained Problems , 1966, Comput. J..
[5] E. Polak. Introduction to linear and nonlinear programming , 1973 .