Quote:
Originally Posted by Bassnut
Yes, terrestial would be different there, but the DL help file mentions this about averaging in binning.
Binning and Resizing
Sometimes it is useful to shrink images. The Binning command does this in the same manner as binning inside the camera – simply combine the adjacent pixels together into a single ”super-pixel”. Unlike a CCD camera, this function averages the values instead of summing them; however the effect is otherwise identical. (Binning inside the camera may reduce total read noise, though, if the binning is done "on chip").
Simple binning does not ensure that the result meets the Nyquist Sampling Criterion. This means that small point sources like stars can all but disappear. The correct way to resize an image is to first low-pass filter it, so that no spatial frequencies exceed one half the new sample interval. This prevents the addition of aliasing distortion into the image. The Half Size command includes such a Nyquist filter. |
That's a great find Fred!
To see how binning by averaging works and why you need an input that is a bit blurry (due to seeing conditions, or artificially introduced by a low pass filter) download
this image (from Bart van der Wolf's excellent
site on comparing downsampling methods).
View it at 100% and it should look like ever narrowing concentric rings. It should be a single ring in the center, with no other rings being visible.
Now software bin (simple bin) this image at 2x2 and look at the result (again at 100%). Due to the presence of high frequency signals in the original, the binning (averaging) has introduced artifacts (you will see multiple rings), however any random noise that would've been present would have been greatly reduced.
Undo everything until you have your original back.
Now perform a simple blur (anything with a 1-pixel radius will do) and perform a 2x2 again. You should now see a perfect binned copy of the original image without aliasing. Again, any random noise that would've been present would have been greatly reduced.
The blur we applied acted as a low-pass filter, to eliminate any high frequency signal from the original (analogous to the slightly blurry image you get when imaging with a CCD that resolves more than seeing conditions permit).
We just simulated the "perfect" situation whereby binning by average can be used to improve the signal-to-noise ratio. Any noise that would have been present in the image would have been averaged and greatly reduced.
This little experiment also shows why the signal-to-noise ratio does not improve by binning willy-nilly (e.g. without suitable source material); yes you reduce the random noise, but you pay for it by introducing artifacts (aliasing).
This experiment also provides a plausible reason for why most people perceive a downsampled (e.g. binned by averaging) image as 'better', regardless of the suitability of the source; the aliasing that was introduced is far less noticeable than the noise in the original image. The aliasing can be quite subtle, even pleasing to human eyes. However, it is still information that doesn't belong in the image and still counts towards noise. It's just "pretty" noise.
So to recap;
Got a soft/blurry image at a high resolution and want to get rid of some noise? Perfect! You don't have any detail to lose anyway and your image is sufficiently blurred to counter any noticeable aliasing. Simply bin it by averaging.
Got a crisp image, but don't mind losing some detail (and resolution) to get rid of some noise? Blur it, then bin it by averaging (or choose a binning algorithm that does both steps for you - like Fred found in Maxim DL).