At one point in time we’ve heard the saying that we are all made of star dust. Therefore, our home , the Milky Way, filled with 250 billion stars should be rather dusty. Right? Well it is, and one famous dust lane that we often see even has a name: The Great Rift.
Say that you are out camping this summer, and you spot the MilkyWay as you are amazed how many stars you can see when away from the city. You remember you have your camera and decide to setup for some long exposure shots to capture all this beauty (lets go for 20 seconds at ISO 3200 17mm F4.0) pointing to the constellation Cygnus. A bit of processing and you should get something like this.
Not bad! Lots of stars… a brighter band where the Milky Way arm of the galaxy is located and some darker spots at various places. Those darker areas are gigantic dusk clouds between Earth and the arms of our spiral galaxy that obscure the background stars. If only there was a way to remove all those stars, you could better see these dark areas.
And there is a way to remove stars! It’s called StarNet++, takes a load of CPU power and works like magic to remove stars from photos. Abracadabra!
Behold! The Great Rift! Well actually just a portion of it. With the camera setup I get at most a 70deg field of view of the sky. Nevertheless, the finer details of these “dark nebula” can be appreciated.
Stripping the stars from an photo does have some advantages: it allows the manipulation of the background “glow” and dusk lanes without concern to what happens to the foreground stars. The resulting image (a blend of both the starless and original image) had improved definition of the Milky Way, higher contrast and softer stars that improve the visual appeal.
While there are plenty of stars above us, what defines a nice Milky Way shots is the delicate dance of light and darkness between the billions of stars and the obscuring dust clouds.
13 x 20 sec (4min 20sec integration time)
17mm F4.0 ISO3200
Deep Sky Stacker
IRIS for background gradient removal and color adjustment
GIMP for final processing