Mike Salway of Ice in Space posted some raw images of Jupiter he took from Australia on the Bad Astronomy / Universe today forum. Mike is the premier (in my view) planetary imager. He provided the exercise in two parts. He posted TIFFs on the first, and TIFFs and AVI sequences on the second.

My first attempt on the first image didn’t get the best results, but I did have fun using PixInsight to process the data.

Since Mike suggested Registax and Photoshop, I decided to be contrarian an use PixInsight, a currently free image processing tool. It has a pretty steep learning curve, but it provides great control over many image enhancing tools.

So, complying with your direction to share processing steps, here is what I did.

1. I ignored the direction to align each color channel. I know this is being lazy, but there was no easy way to do it. In the final image, I don’t see differences in alignment for each channel.
2. I combined each color image into an RGB image, factoring each color evenly.
3. I extracted a luminance channel using “Extract Channel” in PixInsight. I used this to get to the best wavelets for the image, based on the tutorial on the PixInsight website.
4. Finding the right parameters for the Atrous wavelet tranform, I was still not satisfied. I created a mask using a severe wavelets transform of the planet. Parameters were: Levels 8 & 9 (scale 128 & 256) bias 1.0 and 0.1 respectively. All other layers disabled. I then stretched the resulting with curves, eliminating the low end, to create the final mask.
5. Using the mask, I applied wavelets to the entire image. The parameters were, by level: 1: off, 2: +12, 3: 0, 4:+0.8, 5:+0.6, 6: 0, 7: 0, 8:+0.3, Dynamic Range Adjust: High: 0.4
6. Still using the mask but using it inverted, I darkened the limb of the planet. This improved the “flattened” look created by the sharpening process
7. I applied an overall curves (no mask) that increased contrast and boosted saturation. The ability to manipulate hue and saturation in the curve dialog is a great strength of PixInsight.
8. I used the GreyCStoration noise reduction algorithm to smooth out the noise in the image. I increased the scale of the noise to 1.2 pixels, all other parameters were default.
9. I saved the file as a FIT (PixInsight works in 32 bit floating point, so saving in FIT saves all the data) and a TIFF for Photoshop (16 bit integer).
10. I saved the file as a PNG from Photoshop. No manipulation with that tool. I tried an high-pass filter, but it didn’t really help.

Here is the final result:

Unfortunately, while I felt that I got the contrast enhancement done well to bring out the clouds, it lacks color and has an over processed look.

For version #2, I took an approach that let to a much more subtle version.

1. Convert the tiffs to grayscale, save as FITS
2. Open in CCDStack, align the central region
3. Open aligned images in PixInsight, color combine with equal weighted colors
4. Apply a curves transform to increase the contrast, also use the unique ability in PixInsight to enhance saturation with a curve
5. Apply a Weiner Deconvolution using the standard settings
6. Adjust contrast with curves
7. Color balance with the histogram tool
8. Another Weiner deconvolution, using Std Dev of 1.5
9. Yet another curves to add a “roundness” to the planet
10. In Photoshop, save as a JPEG at 150% of original size

Version #2 (resized in HTML for the blog)

For this third and final version, as in my other attempts, I relied almost exclusively on PixInsight. Most of the processing below was done in a 64-bit space. While the original data doesn’t have that granularity, processing with this precision eliminates any rounding errors. Hey, it might not make any difference in this case, but it is cool knowing it can be done.

Here was my process:

1. Open each image in PixInsight, change to grayscale, save as FITS files
2. Open in CCDStack, align based on central region, save again
3. Color combine in PixInsight, using LRGB combine (w/o L) and an even 1/1/1 R/G/B balance
4. Apply a curves transform, generally darkening the image
5. Perform a Weiner deconvolution, 2.75 st. dev, 1.8 shape. This brings out the features in the planet’s clouds.
6. Another modest curves to adjust contrast and a curve on color saturation (a PixInsight feature) to boost the color.
7. Another modest Weiner deconvolution, 1.5 st. dev and 1.75 shape
8. A very modest GREYCstoration noise reduction (0.2 magnitude)

The third and final image looks like this:

It was great fun working with data the quality of Mike’s. I think it may be time to upgrade my planetary camera. The ToUCam just isn’t delivering the image quality, and now I am spoiled.

Back on May 12th, the Mar Vista Clear Sky Clock was predicting a nice evening, so I set out to do a night of imaging. After a bit of searching using The Sky and CCD Navigator, I settled on M63, the Sunflower Galaxy in Canes Venatici.

One of the major struggles was finding an object with an adequate guide star. The brighter the guide star, the more frequent the guiding using the AO-7, and the sharper the image. The brightest star in a usable location for M63 is magnitude 9.83 — really quite dim. It required 1 second guide exposures, longer than I have done with the AO-7 and the C-11.

The day was quite hazy, poor transparency in astro-speak. But poor transparency often is accompanied by good seeing so I went ahead anyway. I got the camera set-up, the scope aligned, focused, found the guide star, etc. and started imaging around 9:40pm.

I took 5-minute clear filter shots and 3 minute R/G/B shots binned 2×2. The poor transparency made the effects of the severe local light pollution worse. The shorter RGB shots reduce the impact of sky glow on the image.

The clear filter does not block the near infrared light (NIR) that a luminance filter would block. I have been using the clear filter based on comments I read to the effect that one can get good data in the NIR so I have been imaging with a clear rather than a luminance filter. That being said, in this case it is a moot point because I am also using a Hutech light pollution filter which blocks the NIR. In the process of writing this post, I found an article by Don Goldman concluding that, for galaxy imaging, one should use an L filter. But I digress.

The imaging went fairly easily. The temperature was stable, staying at about 50 degrees. If the temperature falls too fast, one needs to refocus frequently. I use Astrodon parfocal filters so I do not need to refocus between each filter. The galaxy transited at 10:51pm. I stopped imaging and took a set of flats. When I was rotating the camera to get the guide star back, I felt the clutches slip a little bit, so I needed to shut things down and restart the scope with a “last alignment.” The pointing was OK, but I forgot to turn PEC back on, so the guiding for the two clear images taken after the meridian flip was not that good. That was the one GRRRR moment during the image. I finished up late, after 2am, took the final set of flats and went to sleep.

It took a while to get around to processing the data. I left the following Monday for a week in Dublin for a business meeting, so there was no time to work with the data until I got back. Here are my processing steps:

1. Apply darks, flats, and biases to all frames (there were 51, 18 clear, 11 each RGB, but I had to throw out one blue image because of a satellite pass) in Maxim DL
2. Remove blooms from the images using Ron Wodaski’s bloom remover plug-in in Maxim DL. I tried to use the bloom remove in CCDStack, but it does not work for me.
3. Aligned, sigma-rejected, and combined (summed) each set of clear and RGB images and then the final LRBG images using CCDStack.
4. In PixInsight, I stretched the image to bring out the galaxy (a raw FITS file fresh from the camera need contrast stretching to make the object, stars, etc, visible). I used dynamic background extraction to extract the gradient from light pollution in each image and then used pixel match to subtract it from the base image. Note: If you are not careful and use the default 2x downsample in background extraction, you can end up with half sized final images. I tried the automatic background extraction, but the dynamic process worked much better.
5. I then color combined the object in PixInsight. This involves creating a blank RGB image of the appropriate resolution, and using the LRGB combine process to create the combined image.
6. Using the PixInsight histogram stretch, I balanced the colors in the image. This tool is very powerful in PixInsight. You can manipulate the histogram with great detail and with very detailed feedback as to what your changes are. It even tells you how many pixels you have clipped, so you can manage that finely as well.
7. With the new HDRWaveletTransform process in PixInsight, I brought out the details of the galaxy, and then adjusted contrast with a curves adjustment. To do an effective transform, you need to mask the stars so they don’t get bloated. I created two masks: a star mask and a galaxy mast. I subtracted the galaxy mask from the star mask (the original included some details from the galaxy) and ended up with a decent mask.
8. From here it was into Photoshop, where I applied a minor high-pass filter (8 pixels, blending mode Soft Light, opacity 41%), and adjusted saturation (+13) and color balance.
9. Back to PixInsight for noise reduction. The newer GREYCstoration did not do a good job on the noise in the image. The ACDNR process, however, allowed me to focus the noise reduction on the color part of the image and did a very nice job without losing any details in the image.

Here is the final image:

On the plane returing from the SAP Sapphire conference in Atlanta, I was reading Sky & Telescope. In the section on Exploring the Moon. This is a regular feature and I’d link to it, but they don’t carry the story on-line, or at least not yet. It seems that is the magazine only available to April and I was reading the May edition.

It was an interesting article about the discovery of small craters on the floor of the crater Plato. Plato is a large, flat-bottomed crater near the top of the Moon, in about the center. This discovery, the article said, started a great series of discoveries of other craterlets and even transient phenomenon. At the time, people believed that the craters on the Moon were volcanic; today we know they are impact craters as the Moon has been dormant for several billion years. Altogether fun to read about the craterlets and something to look forward to seeing.

I had that opportunity last night. Having arrived in late afternoon, I was home for dinner. After dinner, I was outside with my daughter playing and I noticed the Moon. Here was my opportunity. I opened the observatory and started up the scope. There were some high clouds, so I didn’t expect great seeing and therefore I did not worry too much about scope cool down.

The Moon was one day after the first quarter, with the terminator just past the mid-line. Observing with a 22mm eyepiece (127x magnification), Plato was nicely visible, just on the light side of the terminator. Plato looked flat at first, but at select moments of still air, a craterlet would appear. It was quite exciting to see something I had read about just hours earlier. I really only saw one craterlet. At higher magnification, using a 3x barlow, the poor seeing was much worse, and the craterlet winked in and out of visibility. I can understand why people saw these objects as transient phenomenon.

I had not planned to image that night, but the web cam was handy and easily set-up. I took one AVI of about 90 seconds. After processing in Registax, PixInsight, and Photoshop, I got the following result.

There are six craterlets that I could find, four easily. These larger craterlets are from 1.7 to 2.4 kilometers in diameter.

The right side of the image is sharper because I had real trouble with Registax’s tracking and aligning except when I used a single, very large alignment box. That selected frames that were clear on the right, without regard to the clarity on the left. Hence, the right is clearer. In addition, I used sigma reject in Registax to reduce the effect of a bad pixel on my web camera.

On the question of "does the photo look like the real thing" I don’t think there is an easy answer. With long-exposure astrophotos such as the Eskimo Nebula from Kitt Peak, this beautiful shot of NGC 1365 on APOD (credit SSRO), or even my shot on NGC 891, there is always an element of judgement in how the astrophotographer made it look. While you can set standards on star color so your color balance reflects the true spectral colors, judgement in processing I think plays a key role.

We can leave aside narrowband or non-visible electromagnetic raditaion images. Reality vs. perception for those is like guessing what things look like to a bee or a cat.

At the Advanced Imaging Conference in San Jose last November, I listened to a discussion between David Malin (of the Anglo-Australian Observatory) and Jerry Bonnell (of NASA/USRA and co-editor of APOD). The conversation had started from the question "Does the photo look like the real thing?" in reference to astrophotos.

Malin said that, if processed correctly, the image would be the same as if we were able to turn up the magnification and sensitivity of our eyes. If we were out in space in front of the Crab Nebula (M1), we would see what the image shows us.

Bonnell, on the other hand, said that we don’t really know what things would look like if we were there. The density of the light is such that were we close to an nebula, it could be so faint as to be invisible. That even "turning up our eyes" would not necessarily yield the same colors regardless of tuning based on spectra.

My apologies to Malin and Bonnell if I have mistated their positions — this is as I recollect it.

I tend to favor Bonnell’s opinion, although I am certainly not one to pick an argument with Malin! I know that when I put together an astronomical image, I do just that — put it together. Separate images for R/G/B and Luminance, darks and flats applied, sigma reject used to reduce noise and multiple images summed, finally combining into a full-color image. Then comes sharpening with high-pass filters, digitial development, and wavelets, all that affect the relative contrast of objects in the image.

While no data is added, with all these steps I cannot assert a connection to "what it really looks like." I try to make it look pleasing to the eye, but I don’t know if it is accurate. And unless we actually go out there, with probe or spaceship, I don’t think we will know what it looks like.

Certainly good science can be done and we can know many facts about astronomical objects. Visual perception, on the other hand, is so subjective I don’t think we can say what it looks like.