Image Combining Methods
Mira provides many mathematical choices for combining a set of images. This command produces a final image in which the pixel at each coordinate is a mathematical combination of the pixels at the same coordinate in all of the source images.
In considering the combining methods described below, keep in mind that each method works by processing all the values at each (column,row) location in turn. At each point, the value is drawn from each image and combined using the selected method to create 1 output pixel. This is repeated for each point until the entire image is processed.
These methods vary from simple linear pixel merging methods to non-linear weighted averaging methods. Use them to Combine Image Set into a single, higher image having higher signal-to-noise ratio.
|
|
|
|
Mean |
Creates an image containing the arithmetic mean value over all images at each point. 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 - Masked by 0 |
Creates an image containing the mean value of pixels from all images except those with a value of 0, which are interpreted as "don't use". 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. This also can be used in combination with the Apply Pixel Mask command. |
|
Sum of Values |
Creates an image containing the sum of the values from all images at each point. If using this method, be sure the output pixel 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 pixel type to 32-bit integer or 32-bit real. |
These methods remove deviant pixels from the sample at each point. The result is a "cleaner" image of higher signal-to-noise ratio and without abnormally bright or dark values. Use these methods to Combine Image Set when some of the pixel values are not from the same statistical population as the majority.
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Mean - Alpha Clipped |
This is a general case of the common 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. This method works well for bad pixel rejection using a number of images significantly larger than the number of high + low pixels to be rejected, N(low) + N(high). If the number of images is smalles than around N(low) + N(high) + 5, do not use this method. |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Mean - Sigma Clipped |
Creates an image containing the sigma-clipped mean value over all images at each point. Sigma clipping discards high and low extreme values in a way you can control with the clipping Properties. 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. |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
These methods create a single image in which each point contains the ranked value of pixels in all images.
|
|
|
|
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. |
|
Minimum |
Creates an image containing the minimum value of all images at each point. This is a good way to bright artefacts 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. |
Combine Image Set, Combine Files, Register Images, Image Region Statistics, Apply Pixel Mask, Statistics Properties