Contents 

Mira Pro x64 User's Guide

Region Statistics Estimators

Mean

Calculates the arithmetic mean value of all pixels inside the reference region with no rejection of extreme values. This is the preferred method if the region contains only well-behaved statistical noise.

Mean - Min/Max Clipped

Creates an image containing the mean value at each point after rejection of the minimum and maximum values. At each point, the minimum and maximum values are not included in computing the mean value. This is an excellent way to remove noise by discarding only 2 of the total number of values being averaged. In this method, even the statistically insignificant deviations, or "true noise" at the extremes are rejected from the mean value. This method works well for bad pixel rejection using a large number of images in which it is likely that a dark or bright non-noise pixel is likely to be found at most locations. If the number of images is small, e.g., < 5, do not use this method.

Mean - Alpha Clipped

This is a general case of the Min/Max clipping method. Here, you specify the number of high values to clip and the number of low values to clip. In comparison, the Min/Max method rejects only the 1 highest and 1 lowest values.

Mean - Sigma Clipped

Computes the arithmetic mean after iteratively rejecting extreme values more than sigma's from the mean value. The rejection is 2-tailed, and the upper and lower sigma multipliers are specified independently. For example, setting High Sigma = 2.5 and Low Sigma = 5.0, then the statistic is computed using values within 2.5 sigma's above the mean and 5.0 sigma's below the mean.

Median

Creates an image containing the Median values of all images at each point. This method has good ability to reject extreme values. For a given number of input images, the noise in the resulting image is not as low as that which can result from Mean combining methods.