Mean

Creates an image containing the arithmetic mean value over all images at each pixel location. The Mean method combines the pixels as a straight average with no weighting or rejection of bad values. This is the preferred method if the images can be considered to contain only well-behaved statistical noise.

Mean - Min/Max Clipped

Creates an image containing the mean value at each pixel location after rejection of the minimum and maximum values. At each pixel location, 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 images being combined. 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 - Masked by 0

Creates an image containing the mean value of pixels from all images except those with a value of 0. Pixels with a value of zero are not included in calculating the mean value. See the Register Images command for an application of this method.

Mean - Sigma Clipped

Creates an image containing the sigma-clipped mean value over all images at each pixel location. Sigma clipping discards high and low extreme values in a way you can control with the clipping parameters. This method requires a large number of images, on the order of 20 or more in order to compute good clipping criteria at each coordinate.

Median

Creates an image containing the median values of all images at each pixel location. 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.

Minimum

Creates an image containing the minimum value of all images at each pixel location. This is a good way to bright artifacts that affect a large fraction of the images at the same coordinates. For example, if 5 of 8 images have large bright areas or defects, you can remove them using this method. The advantage over simply throwing away the 5 images is that there can still be some filtering done to areas that do not show the bright defects.

Sum of Values

Creates an image containing the sum of the values from all images at each pixel location. If using this method, be sure the output data type has greater sufficient numeric range to handle the resulting values. For example, combining 100 16-bit images having peak luminance of 50,000 may result in some output pixels being as high as 5,000,000. Clearly 5,000,000 is not within the 0-65535 range of a 16 bit unsigned integer image. To handle this change the output data type to 32 bit integer or 32 bit real.