1. Take RAW images (light, dark and flat frames), 2. Develop all the RAWs with ImagesPlus, linear mode, daylight white balance, into 16-bit TIFF files, 3. Average combine darks into a master dark and flats into a master flat (I don't use flats very often but sometimes they are crucial), 4. Calibrate light frames using the master dark and master flat, 5. Align calibrated light frames, 6. Combine aligned frames (adaptive addition or some of the variations of average combination), 7. Enchance the images (Digital Development, Levels, iterative restoration, sharpening, etc.) What happens here is that the master dark and flat remove all constant problems in the image. Calibrating with the master dark does actually increase the S/N ratio, but too little to be noticed, because it removes constant hot pixels only. Master flat does not change the S/N ratio but it rather balances the brightness of the center with the corners as well as removes dust effects. Stacking improves the S/N ratio the most and makes it possible to perform more drastic processing later on without making the noise visible. Besides, if you (manually) enchance images before stacking, you will need to perform it multiple times. It is much easier to do after stacking, because there is only one image!
ImagesPlus does a nice job with its Adaptive Addition where overflow is automatically avoided. When the signal level is high enough to begin with, I use averaging or some of its variations, but when the signal level is low, like for nebulae, I use adaptive addition. In fact, I use it a lot, because even for globulars and open clusters it helps to boost the dimmest parts of the image. Adaptive Addition in ImagesPlus not just adds but it increases the level of the dim parts of the image more in proportion.
As the software engineer explained it to my on the phone, Mira Pro is able to detect a gradient (which he referred to as a slope of linear data) and remove it with some complicated math that he tried to explain. I lost him halfway into it… You are able to specifically define regions that should not be used in the computation of the background data. Now, how do you tell if you removed the gradient, or where the gradient is in the first place? Mira has a powerful feature that allows you assign a color palette to a grayscale image so that you can look at your data in different color spectrums. By fine tuning the histogram stretch and the color palette contrast and gamma, you can get views of your data that reveal gradients in stunning color. For instance, lets say you are looking at your full screen nebula region. You would assign an aggressive stretch on the grayscale image, then assign a color palette to the image, do a little tweaking, and low and behold, one corner of the image is bright red fading to bright blue on the opposite corner of the image. Now you use the gradient removal tool (which they call fit background) by selecting regions to exclude from the calculation, set your math options, tell it to maintain the value intensity in the central part of the image, and click OK. You then can look at the false color data and determine if the field is perfectly flat. If it is not, you can undo, and do a little more tweaking of the fit background dialog until you get it right. At one point I was looking at M20 which filled the entire field. M20 was bright green, the background was bright blue, with a bright red gradient running through the background of the image. I selected the central brightest portion of the M20 and clicked on the fit background tool, and it perfectly removed the gradient, creating a perfect blue background with no red, although all the dim nebulosity stayed bright green (and yellow) at the brightest points. I did this on all four channels. It was magic. That's the only way to explain it. I also tried a image from the FSQ-106 and STL11000. A huge nebulous star field. The gradient popped out in living color. In grayscale I could not see it even with the most aggressive histogram stretch. In addition to that, you can load all four channels into an image set, where you can flip through the four images at 1-30 frames per second as an animation. Make real time histogram stretches and color palette adjustments that apply to all four channels at once and easily see the gradient differences in each channel. I could go on forever about what a great tool this is for *the perfectionist*…but I only know about 10% of the program so far. ;) $50,000 for imaging equipment…crummy pictures from gradients. $50,000 + $1300 for software, and your images come out nicer and more accurate with easy tools to repair data due to light pollution. Now its not all that easy, but we spend less than $1300 on three emission line filters. ;) There is a lot more to image processing than gradient removal, but I will tell you, without gradients, image processing becomes much easier. The more time I spend in Astrophotography, the more I realize that imaging raw data is the easy part. Its what you do with it once you have it. This is where software comes in. People have a hard time justifying expensive software purchases because it does not weigh 47 lbs and breaks your back putting it in your trunk…but if you think about it, software for image processing has a far greater impact on the final product of all your labors than the equipment you use. Give me an 8“ LX200 and a ST7XME and put me up against a 14” RC with a ST10XME. If the owner of the 14“ RC cant image process, then I produce the finer image. My point, think of software as being as valuable as your imaging equipment and then you start getting the proper perspective on value. I would rather own a 130mm refractor and lots of great software (and RAM), then a 180mm refractor and Paint Shop Pro.
rb from Mt Ewell Observatory July 2004
I agree one hundred percent with your philosophy. It is a lot like buying a telescope. Many new to this hobby and just starting out, buy such things as a LX200GPS-12. (Yours truly included.) They think that the big pretty OTA will allow them to see Hubble type vistas right in the eyepiece. The mount is only that part of the system that holds the telescope up so you can look through it! I now tell anyone who is thinking about getting into this hobby to buy a GOOD mount then look for an OTA. If I were doing it again, I would have bought something like a Losmandy GM-11 and put my old C-8 OTA on it until I could afford a larger OTA. (It would have been less expensive too.) Buying software is much the same. I used Paint Shop Pro for a long time (And I still like some of its tools.) But PS-CS allows me to do things that PSP simply does not have the power to do. This is what the extra few hundred dollars i n purchase price buys you. I suppose the same thing is true about Mira. You can use lower end programs and still come out with very good images, but the higher end tools produce the images with a lot less aggravation. Don Waid
I have seen so many people plan their hardware budget carefully to get a mount, scope, pier, reducers, focusers, etc., and have no clue that they will eventually spend another $2000 or more in software to process their images. They also have little insight into the process work flow and how many disparate programs that they will need. For example, 1. Acquire your data with CCDSoft and do image links with TheSky to find the guide star. Get Focusmax software to focus your electronic focuser for sharp stars. 2. Use reduction groups in CCDSoft (now Maxim, too) to reduce and possibly align your images. If you don't like their alignment quality, get other programs like Registar or MIRA. 3. Combine the reduced images in such programs as Sigma or Russ' Croman's RC Control Panel to take advantage of more sophisticated rejection methods. 4. Deconvolute the luminance in programs like CCDSharp for even sharper stars. 5. Bring the R,G,B into Maxim, normalize the background and apply the color weights after aligning. Oh, you may have used alignment in CCDSoft or gone out and used programs like Registar. Create a 16-bit RGB TIF file in Maxim for import into yet another program, Photoshop. If you don't own it yet, prepare to give up 2” Nagler eyepiece and buy Photoshop, and if you don't have Photoshop CS to work in 16-bit mode, better plan to upgrade $$. 6. You may still may go back into Registar to align the RGB TIF with the FITs luminance. 7. Import into Photoshop. Oh, wait, the FITs file is not importing. Get Eddie Trimarchi's free FITs plug-in for Photoshop. If you are working with 32-bit real (IEEE) files in such programs as MIRA, then you need to buy Eddie's commercial program. 8. Spend 3 years learning Photoshop (Total Training's DVD set is wonderful in this regard $$), and possibly buy Grain Surgery to smooth backgrounds. 9. Make sure you budget for a laptop or desktop computer with the largest hard drive you can find and a fast (e.g. 3GHz) processor, and don't forget that large-screen calibrated monitor. 10. And……… I know this is a generalized example (rant) just to make a point, and is nowhere near exact. There are many other programs out there, such as Image Plus, MIRA, AIP4WIN, Picture Window Pro, AstroArt, etc. just to confuse your selection. Have you ever tried flowcharting this???? Anyway, I was one who did a good job budgeting for the hardware with no clue as to the software. As Richard points out, it is your processing skills that will make your raw data alive, but you have to figure out your process flow first. Don Goldman www.astrodon.com
Another method to remove gradients:
The information for gradient removal is in the picture, when it is a many stars–much sky type picture. My backgrounds were effectively removed by doing a wide range median fiiltering on my original picture and subtracting it from the original. Of course, in many cases there are structured foregrounds which make this procedure difficult and artistic talent is necessary to make a structureless background. In case of Hi-Res pictures, it is not necessary to do a median on a full-scale picture (which took ages, especially on a 33 MHz 486). Resampling the picture for 10 % of the size, median filtering it and resampling to the original size was just as effective. Siebren Klein firstname.lastname@example.org http://www.geocities.com/siebren2001/index.html
Manual normalisation: To balance the color in a RGB image you need to have all the images “normalized”. Roughly speaking, and this may not be technically correct, you need to set the background level of the images to the same value. As you can see from my post the background ADU counts for my R G & B were all very high and they were different. This is primarily due to the high level of light pollution I was imaging under. This light pollution is not even across the spectrum and therefore the different levels of background intensity. This of course is also affecting the main part of the image but you do not have a standard there to go by. The standard to balance to is a dark sky location. In effect, the normalization process is to remove as much of the light pollution (Sky Glow) effect as possible. This is not to be confused with correcting a gradient problem. That is a different process. To normalize the RG&B frames I use pixel math in MaxIm. I make note that the sub-exposures ha ve been reduced and combined into 3 R G B master frames. If any gradient removal plug-ins are used I do that before normalization. Now for the normalization steps. First open all three R G B master frames into MaxIm. Open the image information window. (View/Information) Select a master frame to work on. Choose three locations on the master frame where you know the sky is to be dark. These will be our standardization reference points. (Avoid any nebulae, stars, etc.) I set the aperture radius of my cursor to 10 pixels. (Right click on the image and choose “Set Aperture Radius”) Move the cursor over the three areas you chose and read the average ADU count displayed in the Information Window for each location. Get out you calculator and average these readings. This now becomes the background count for the frame. My R frame had a count of about 9,200. (Very high, I hope yours are not that bad.) You now go to pixel math. (Process/Pixel Math) I try to bring the background count down to about 125 to 150. To do this set the parameters in the Pixel Math Window to: Scale factor % = 100 Operation = None and Add Constant to the amount you want to reduce. In my case it is -9,050. This should give me a 150 background when the operation is complete. Click on OK and the operation completes. You can now check by moving the cursor over the three reference areas and see if they are in the ballpark of between 125 to 175. If not simply go to Edit/Undo Pixel Math and redo the operation again with a revised “Add Constant” amount. Do this for all three of the R G B frames. Just be sure you use the same three locations for all frames to get your background counts. After you normalize your R G B frames you can combine them into a master RGB image. I combine this RGB image with my Luminance image in PS layers. I know this is long and I am no expert in image processing. Some on this group may wish to educate me as to a better method of doing this. This is just what I use and I learned a lot by trial and error. (It seems like more error than anything else.) Use this and if you like it all the better. If it doesn't work for you, disregard or modify it. If you find something else works better please let me know so I can use it. Don Waid
Here ya go: 1. Open all three R,G and B master FITS frames in Maxim. 2. Start with the Red frame. 3. Open up the information window. VIEW > INFORMATION or Ctrl-I. 4. Set mode to APERTURE. 5. Right click on the image and set APERTURE RADIUS to 10 pixels. 6. Now grab your notebook or a piece of paper. 7. Find three spots on the image that are most obvious background areas largely unaffected my your object and stars. You may need to adjust your APERTURE RADIUS to accommodate for a very busy image. 8. Note in the information box the average ADU count for those three areas. You don't have to use three, you could just use two or even one. What I normally do is just scan the image for all the background areas and get a 'feel' for the background ADU count and then decide on a number that I think represents an accurate ADU background number. I think our brains can do a better job than the computer on figuring what is background and what is not based on what we see with our own eyes. Remember to stay away from obvious gradients and hot pixels and dark areas while doing this. Once you get the hang of it, you will be a background ADU expert. ;) 9. Pick a number and right it down in your notebook. Round up to the nearest 50. So lets say you look around three areas of the image and they all hover around 5012-5055. Write down 5050 as the background ADU for that image. 10. Next open up pixel math. PROCESS > PIXEL MATH. 11. Image A should be the Red frame we are working on. 12. Image B should also be the Red frame. 13. Operation is add. 14. Add constant should be 100 pixels less than our ADU number we came up with. So if we came up with 5050. The add constant should be -4950. 15 Select OK. 16 Now go to the corrected (normalized image) and confirm that the areas you were studying all hover around 100 ADU. +/- 20% is expected. 17. Now repeat these steps for the Green and Blue images using the same areas you measured ADU in the red frame. You don't want to use new areas in these images as that would defeat the purpose of getting all three images normalized. 18. Now you have three manually normalized RGB images. 19. Combine those using Mr. Goldman's fine RGB ratio's, (Don't forget to uncheck normalized images when combining), and you will find that color balance comes our very well. 20. When you save the image, make sure to save as TIFF file and that the file is stretched for 16 bit. (Under SAVE AS, select STRETCH, select LINEAR ONLY, INPUT RANGE as MAX PIXEL, OUTPUT RANGE, 16 BIT.) 21. Import TIFF into Photoshop and use levels and curves to bring out the image details. rb Richard A. Bennion Managing Director Ewell Observatory http://www.ewellobservatory.com
One way to manually normalize (per Ron's first book) is to use pixel math. Say that your red channel has a background adu count of 1000, your green is 1500, and your blue is 800. Then assume you want to bring down the value so _all_ 3 channels are at 100. In Maxim, choose pixel math and select the “add constant” feature. Then plug in a negative value for the red of -900, the green of -1400, and the blue of -700. When you have done this, the background adu of all three channels will be around 100..and the background should look neutral when you combine the 3 channels (light pollution gradients aside). Actually what I do is use software on each channel to take care of light pollution gradients first, then do the pixel math. Another way…the way I think Ron now uses and is in his new book, is just to do the RGB combine and select (click on) normalize background. Then Maxim will do a reasonable job of creating equal background counts for each channel. You would then bring this into PS and do final tweaks on the black point of the histogram of each channel so that the space between the left point and the starting point of each channel's histogram is equal. I prefer this method because it's a hard core assurance that the background will be neutral. (I have problems distinguishing between dark colors, so I rely on the histogram routine to make sure things are “right”) Once the background is “neutral”, color balance on the “target” becomes easier, since you are now dealing with just the color balance tool (minor tweaks if your RGB combine ratios were correct for you system)..or you can adjust the “target” color thru histogram changes in the midpoint and white point “pointers” for one or more channels. (after doing either of the above, you may need to go back and tweak the background black points again to maintain a neutral background after the tweaks. Hope this makes sense…if not, please feel free to ask more questions…maybe I or someone else can explain it better.
Incidentally, the reason that both Richard and I are shooting for a 100 adu background result is that we don't want to go below the “pedestal” set by SBIG and others which is usually 100 adu. If you normalize to a number below the pedestal of 100, the resulting histogram will look clipped. (no space between the left point and the starting point of the histogram). Randy Nulman