auto-correlation function

"Auto-correlation function" is a function to give information about the shape of the function such as sharpness, roundness, periodicity, etc. It is a function (or a pattern) that is acquired by integrating the product (overlap) of two same functions with respect to a variable which are shifted each other by a certain amount about the variable. That is, when we define the object function f, an integral variable of the function X and a relative shift of the function x, the auto-correlation function R_ff can be written as the following equation: R_ff=∫f(X)f*(X-x)dX. Note that * denotes complex conjugate. In the case of a microscope image etc., f is a real function and then f*(X-x)=f(X-x). If the value of R_ff is large even for a large shift, the original function (or the original pattern) is delocalized in the X direction. Contrary, if the auto-correlation function decreases rapidly with increasing the relative shift, the original function (or the original pattern) is localized. If the function is peridic, R_ff takes a large value at integral multiples of a certain x. Thus, the periodicity of the function is obtained. Therefore, the auto-correlation function enables us to obtain the knowledge about the shape of the function or the pattern with respect to the variable. For example, by calculating the auto-correlation function of a TEM image of a particle, the amount of deocus is estimated from a blur of the image and the amount of 2-fold astigmatism is obtained from an elongation of the image. For high-speed computer calculation of the auto-correlation function, FFT (fast Fourier transform) is used on the basis of the following theorem: Fourier transform of the product of certain functions is equivalent to the product of Fourier transforms of the respective functions. That is, the auto-correlation function is calculated by the inverse Fourier transform of the power of the Fourier transforms of the respective functions.

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