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Southern Cross - July 2004
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Creating Mosaic Images of CometsVello Tabur After the last monthly meeting, Ross Gould asked me to write an article describing how I created the LMC / NEAT mosaic image. I wanted to capture the comet and the LMC in a wide field shot and it would have been easiest to use a short focal length camera lens, say of 80mm focal length, attached to my CCD camera. Unfortunately I have overhead power lines near my observatory which would have ruined the image, so I chose to build a mosaic of smaller (but higher resolution) images. I normally use a Nikon 80-200 mm zoom lens set to 140 mm, which gives a FOV of 5.7 x 3.6 degrees. The camera uses a Non-Anti Blooming Gate (NABG) detector. The advantage is that it’s more sensitive than an ABG chip so it picks up more faint nebulosity. The down-side is that bright objects will saturate fairly quickly and leave a blooming spike, the length of which depends on the degree of saturation. At my short focal length, it starts to become a problem on exposures longer than 30 sec, or when very bright objects are in the field. Luckily there were no really bright stars in the target area so I took ten 30 sec exposures of each field. It required a little planning to determine the correct field centres, but no great precision was necessary to slew to each field, as I allowed for some overlap. The main priority was to image the segments that contained the comet so that they could be registered without the comet’s movement becoming apparent. The other constraint was that there is a fair bit of light pollution in my SW sky, so each image had to be taken at the greatest possible altitude but still be below the lowest power line (which is at an altitude of about 40 degrees). It took about 1.5 hours to capture all the raw images. In addition to the light frames, I took 25 dark frames. Darks are normal images taken with the same exposure time and temperature as the lights, but with the shutter closed. Heat in the electronics of the detector generates photo-electrons in the same way that photons coming from the sky do. By taking darks, we can remove this source of noise from the images. The darks were combined into a master dark frame with my own software using a sigma-reject algorithm to remove chance cosmic ray hits. Each raw image was dark-subtracted and flat-fielded. The latter removes the effects of vignetting, dust on the optics, and inter-pixel sensitivity differences. Each set of calibrated images were then stacked (co-added to improve signal the to noise ratio) using some commercial software (AIP4WIN). This yielded twelve master frames, each one being a calibrated 300 sec exposure. The stacking process is performed by selecting two reference points on each frame. These are usually well sampled stars that are diagonally opposite each other in the corners of the image. The software rotates and scales the slave images to the orientation of the master and adds them together. One potential problem is that periodic error in the drive or an incorrect drive rate may produce a slight shift on some images so that not all of them contribute equally to the pixels on the outer edge of the image. For this reason, I usually crop the resulting image slightly to remove a couple of rows/columns around the outer edge. Since the images were taken in a light polluted sky, each one contained an obvious gradient (brighter towards the horizon). Trying to combine them into a mosaic at that stage would have (and did) produce an unsightly mess, so the next step was to remove the gradient. Luckily I already had some software that could do this, although it had to be enhanced a bit. The basic technique is to place an imaginary grid (say 64 pixels square) over an image and to calculate the background intensity in each cell. This is done using pixel statistics. Knowing the background for each grid, it is an easy matter to calculate the background intensity for each pixel using a bi-linear interpolation and subtracting an appropriate amount from each pixel. This results in a gradient corrected image; well almost. The gotcha here is that if a cell contains a lot of nebulosity, like a comet tail or within the LMC, the background value is overestimated and subtraction removes the nebulosity too! I changed my program to superimpose the grid over an image and used mouse clicks within selected cells to disable the background calculation for them. This way I could exclude cells that contained the comet’s coma and tail from the background calculation. All other cells were computed as before, and the cells that were excluded were interpolated using nearby cells. This produced a nice result for most images, with the exception of the images that were wholly within the LMC, as they contained nebulosity in all the cells. I was stumped for a while but finally came to the realization that other images containing non-nebulous regions taken at a similar altitude and azimuth to the LMC shots recorded a similar sky glow. Applying the background grid from the most appropriate images to the LMC sections proved quite successful, although a little manual correction (in AIP4WIN) was required. The next step was to arrange the images into a mosaic. Up to that point in time the largest mosaic I had produced was one of Comet LINEAR containing three panels. A lot of people use Photoshop for building mosaics as it allows one to manipulate the images independently in separate “layers” until they are properly registered and then merge them into a single image. However, Photoshop costs about $1000, so I downloaded GIMP (GNU Image Manipulation Program – see www.gimp.org) for nix. It performs almost all of the functions of Photoshop. As Comet LINEAR was imaged near the celestial equator, a rectangular projection suited the images and they were easy to align with up/down and left/right shifts. However, the NEAT images near the LMC were taken at high declinations where a conic projection was more appropriate. This meant that after aligning just two images manually, I found that all sorts of rotation and scaling operations were required to get the rest to overlap properly. Not keen on trial and error, I chose to write a bit of software to fix the problem. First, an astrometric plate solution was calculated for each image. This allows one to translate between RA/Dec and x/y on each image. Then I created a 4500 x 3000 pixel canvas and using the astrometric solution of each image, determined where each pixel should be placed on the canvas using the solution (projection) of the central image. This does introduce a little distortion, but it’s not noticeable in practice, and I was after a pretty picture and not a science grade image after all. The final (heartbreaking) step was to resample the canvas to a more manageable size. The original images showed pinpoint stars down to 16th magnitude with a wealth of detail. But for web publishing and display the canvas was resampled to 1280x1024 and compressed into a jpeg with a further 20% loss of detail. Even then, it still looks pretty good, but nothing like the original. Still, not bad for an ordinary telephoto lens used from a suburban backyard with a 36% sunlit Moon in the sky! |
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