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72899Re: [ccd-newastro] Calculating average brightness in an area of a fits image

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  • Mark Striebeck
    Jun 2 12:27 PM
      Thanks for these great explanations Stan and Ron.

      Let me recap (to make sure that I understand it correct).

      sky glow is a bias that affects different colors differently, lets assume
      it adds (10, 1, 2) in red, blue, green

      my imaging train (mostly camera) introduces a color imbalance, let's assume
      it's red=1, blue=1.1, green=1.2 (i.e. the green signal is 1/1.1 times
      weaker then the red signal)

      Now, let's assume that two pixels in my image have the original colors (50,
      50, 50) and (100, 100, 100)

      Because of sky glow, these pixels will be (60, 51, 52) and (110, 101, 102)
      when they enter my scope.

      My imaging train distorts them to (60, 46, 43) and (110, 92, 85)

      I can't remove the bias first as there is no constant addition/subtraction
      that would work for both pixels.

      Which means, I have to correct the color imbalance first (either by
      selecting different exposure times, or by selecting different weights when
      combining the 3 colors). And after that, I can remove the bias from the sky

      Did I get this right?


      On Sun, Jun 2, 2013 at 11:39 AM, Ron Wodaski <yahoo@...> wrote:

      > I'll add my two cents to this. It's a topic that has been around for a
      > long time, and there are many ways to look at it. I have my own reference
      > points, and I think they are useful ones. There's a long discussion about
      > it in my first book with some visualizations that help show the difference;
      > here's a short version without the illustrations.
      > I call the two different types of color problems color _bias_, and color
      > _imbalance_.
      > Let's start with color bias. Any bias is simply an offset. It applies to
      > voltages and other things, as well as to color levels. So if there is a
      > bias in a color, it means that the _real_ values are offset by the same
      > number in all cases. So if one pixel has a true value of 100, a bias of 12
      > means that the pixel will contain a blue brightness value of 112 instead of
      > 100. Another pixel with a true value of 21 will contain the value 21+12, or
      > 33.
      > To remove a blue bias, you subtract the same amount from the blue channel
      > of every pixel. The trick, of course, is figuring out what that amount is.
      > So a bias is a uniform, unwanted signal that affects all pixels equally.
      > A color imbalance occurs when different amounts of signal are recorded for
      > each channel. In this case, the effect on each pixel is proportional, not
      > fixed. For example, blue brightness might be 20% more intense than the
      > other channels. Using the same brightness values as in the previous
      > example, the pixel with a blue brightness value of 100 would have a
      > recorded value of 100*1.2, or 120. The increase in blue brightness for for
      > a pixel with a blue value of 21 would be 21*1.2, or 25.2.
      > So you can't simply subtract a uniform value to correct a color imbalance,
      > you need to adjust the percentages of each color channel to achieve balance.
      > So one key point is this: color _bias_ and color _imbalance_ require
      > different mathematical methods to be solved. That's important because it
      > establishes the need to address each individually. You can't properly
      > balance color if there is a bias; you can't remove a bias by color
      > balancing.
      > (Not explained here, but you be able to see why on your own: if there is
      > even a very small color bias, changing the color balance will amplify that
      > bias. So you must remove color bias first, then balance color. It's also a
      > good idea to look at the color bias after a color balancing, just in case
      > there was any residual you missed.)
      > There are good tools for each of these corrections. The trick is in
      > knowing how to tell which is which. Looking at the individual color
      > histograms is the best way to spot a color bias: the black point will be
      > different in each channel. (That is, the rise at the left side will occur
      > at different distances from the left margin.) The appearance of a color
      > bias is more subtle; the actual histogram curves will have different
      > proportions. (Note: an image with a lot of one color can mimic this, as
      > when you image a nebula that is heavily dominate by H-alpha emissions.)
      > More to the point of your question, sky glow is 'sky color' - you can't
      > really separate things like airglow and light pollution; they are all
      > simply recorded during your exposure. On the other hand, once you learn to
      > remove bias and imbalance separately using whichever tool you prefer, you
      > will automatically correct for _all_ color issues correctly. The source is
      > not nearly as important as making sure the right math gets applied to the
      > right issues!
      > Ron Wodaski
      > On Jun 2, 2013, at 10:29 AM, Mark Striebeck <mark.striebeck@...>
      > wrote:
      > > Hi Stan,
      > >
      > > Thanks a lot for these instructions (I was just composing an email asking
      > > how to do this? :-)
      > >
      > > But why is it that sky glow affects star colors differently then sky
      > color?
      > > In my (naive) view, I thought of sky glow as a kind of filter that
      > distorts
      > > the color for everything equally in an image.
      > >
      > > Thanks
      > > MarkS
      > >
      > >
      > > On Sun, Jun 2, 2013 at 10:25 AM, Stan <stan_ccd@...> wrote:
      > >
      > >> **
      > >>
      > >>
      > >> Obviously the sky background color pollution was not handled and those
      > >> ratios are invalid.
      > >>
      > >> To calculate color/filter ratios, it is imperative to first subtract sky
      > >> bias levels from each component.
      > >>
      > >> To manually calculate:
      > >>
      > >> For each filtered image, measure the mode or median of nearby dark sky
      > (no
      > >> stars or nebula) and then measure the avg of a tight and uniform box
      > >> surrounding the calibration star (G2); subtract the background from the
      > >> star and use that for the ratio calculations.
      > >>
      > >> To calculate via CCDStack:
      > >>
      > >> Create a color image, open the "Adjust Color" form (if not already open)
      > >> and perform a "set background" (if not done already). Drag a tight
      > >> rectangle around the calibration star and press the "balance" button (be
      > >> sure the "factor" radio box is checked). The ratios are automatically
      > >> calculated and displayed. To normalize ratios to a particular color �
      > >> double-click the area next to the ratio display box for that color.
      > (read
      > >> Help: Concepts, Color section)
      > >>
      > >> Stan
      > >>
      > >>
      > >>
      > >
      > >
      > > [Non-text portions of this message have been removed]
      > >
      > >
      > >
      > > ------------------------------------
      > >
      > >
      > >
      > >
      > ------------------------------------

      [Non-text portions of this message have been removed]
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