Two almost clear nights in two weeks!

And I took at advantage of the second one as well, at least for a couple of hours. It seems like this year has been the wettest or cloudiest one in recent memory. There has still been the persistent high cloudiness even on the “clear” nights, which makes the stars harder to see. I did some imaging anyway, this time of M17 and M18. These are located close to Sagittarius and close to each other; might as well take advantage of making a minimal telescope move. So far, I've been able to process only M17, which is also called the Swan nebula, Omega nebula or Horseshoe nebula. I'll let you decide if you can see it; I can't.

 

 

This image presented a different kind of challenge in the post processing. If you know about taking astrophotos, you know about “hot pixels” and thermal noise. If you are not familiar with the ideas, the easiest image I think I can make for you would be something like this: Imagine taking you camera and putting the lens cover on so that no light can reach the sensor (film, if you are thinking in terms of a film camera. However, this phenomena occurs only with digital cameras, so...). The expectation would be that the “image” would be completely black. It isn't, however. Close inspection shows white specks, like salt sprinkled on a piece of black paper. In actually, the image, if “stretched” (meaning putting the black and white points close to each other,more or less) would look like a snowy tv picture with white dots on it. The snow is the thermal noise and the white dots are the hot pixels. A hot pixel means that the pixel puts out too much voltage (meaning whiter) when hit be a photon. As more of an example, let's suppose that normally a photon that hits a pixel puts out 1 volt (it doesn't, this is just an example). If the pixel has 0 (zero) volts, it is completely black. If the pixel is hit by enough photons to allow it to reach it's maximum voltage, it would be 65,565 volts (or there abouts), and would be completely white. In between, we would see it as a shade of gray on a monitor. There is normally a direct relationship between the number of photons that hit the pixel and the voltage; if 20 photons hit, the voltage is 20 volts. If 1000 photons hit the pixel, the voltage is 1000 volts. With a hot pixel, it might be hit by 1000 photons, but instead of 1000 volts, it puts out closer to 65,000 volts. So, my problem was too many hot pixels in the photo.

So, what do they look like?

 

The red, green blue pixels circled are what they look like. Why red, green, and blue? My camera is a black and white (or monochrome) camera. To get color, I have to take series of photos with red, green and blue filters in front of the sensor, then, as part of the post processing, combine them to make a color image.

There are a few things I can do to help reduce them, like cooling the sensor more. I currently operate it at -5 degrees C, but I think I can get it much cooler. That's on my todo list. But what to do about the photo already taken? Well, in the post processing phase of working on the photos, (post, in this case referring to after the photo has been taken), there is a technique to help reduce the effect of noise by using a median filter. Think of if like this: the noise shows up as a white, or light colored dot of the monitor. If it were a drop of white paint on a black piece of paper, we could diminish the effect if we could smear the drop around the paper. The greater the area we can smear it over, the less noticeable it is. If the photo is 10 megapixels, and 100,000 are “hot”, that's a lot of smearing to do. There is another way to accomplish mostly the same thing. If I resize the image from 10 megapixels to 5 megapixels, the resizing algorithm has to throw away 5 megapixels. How does it chose which ones to throw away? I don't know the ins and outs of the algorithm, but part of it works like a median filter; it basically looks at all the pixels around a single pixel and “throws away” any ones that are vastly different from that pixel. That's how it helps eliminate the hot pixels. How well did it work? It eliminated about 90% or more of them. I think by using a cooler sensor and using this “trick” I should be able to make a major increase in quality of the photos, at least as for as the noise problem goes.