Online Help for Chasys Draw IES: Processes : Stacking : Image Averaging

Processes : Stacking : Image Averaging

What is Image Averaging?

Image Averaging is an image stacking operation that calculates the average value of each pixel in a set of images. Image Averaging is a powerful noise-reduction technique that has the power to remove noise while simultaneously enhancing detail, and is very commonly used in high-end astrophotography and other types of low-light and night photography. In astrophotography, it is sometimes referred to as the “shift-and-add” method and is one of the two forms of speckle imaging used in that field. Unlike other noise reduction techniques, Image Averaging actually increases the signal to noise ratio (SNR) of your image, as opposed to “hiding” noise by selectively removing pixels or filtering high-frequency image components.

When used for noise reduction, Image Averaging works on the assumption that the noise in your image is truly random, which is true for most sources of noise in digital photography. This way, random fluctuations above and below actual image data will gradually even out as one averages more and more images. If you were to take several shots of a wrist-watch using a steady camera with identical settings and under identical but non-ideal conditions, you would obtain images similar to the one shown on the left below (100% crop of larger image). Averaging them gives you the image on the right:


As you can see, most of the noise has been averaged out resulting in a much cleaner image.

Image Averaging is also very effective against haze when coupled with dynamic range control. The additional information obtained by averaging multiple images cancels out the quantization errors introduced during sampling, hence making it possible to increase image contrast (by way of adjusting dynamic range) without sacrificing quality. This technique was used to take the photo of Mount Kenya shown below from a distance of 20 kilometers on a hazy day using a low-cost mega-zoom camera:


This technique is also a very effective way of increasing exposure in low-light photography, where it can extract well-exposed, detailed and noise-free images from exposures that are almost completely dark.


The “how” bit

You need at least 2 same-size images of the same scene to run this engine. The images need to be aligned; this can be done by either making sure the camera remains perfectly still or by using the Align Images for Stacking feature.

This is the GUI for the Image Averaging feature:

The sliders for sharpening and gamma operate on the high-precision summation data to minimize rounding errors. The “outliers” settings determine how many pixels can be treated as outliers caused by noise or other defects and thus omitted from dynamic range calculations. Ten outliers per mega-pixel is usually a good rule of thumb. The cut-off points determine the portion of the range to use; the default is to use the entire range (0% – 100%).

For the best results when stacking images, keep the camera as steady as possible to reduce the amount of alignment that has to be done. If you need to use stacks that are much larger than what Chasys Draw IES can support, please consider using the bundled MegaStack™ plug-in. It can handle thousands of images and does parallel processing on multi-core CPUs.



Copyright © John Paul Chacha, 2001-2024