Everyone has a different take on the same input data.
Astrophotography is an often misunderstood art. Or, science. Or…? That’s the question. In my opinion, you can separate AP based on the processes involved.
Data acquisition is science. At its base level, the data is the data. It’s information, scientific data, and that’s why I love it. There are a lot of ways to interpret data.
Data processing is art. Because of the many different ways one can interpret scientific image data, post-processing is where I think AP makes the split from science to art. As soon we start mucking with the raw data and creating a final image that represents what we, the artist, considers to be correct, that’s where we lose some of the science and things start to get pretty.
It’s not the hammer, it’s the carpenter
Don’t ever for a second think the answer to your sub-par images is to throw money at the problem and upgrade all your gear. Throw away the idea that when you get a telescope and camera that cost more than your car you’ll start seeing images rivaling those from the Hubble. You won’t.
Instead, replace those wrong thoughts with time, patience, and practice.
Post-processing skills are vastly more important than the gear you’re shooting with! Sure, gear matters to a point, because bad input is bad input, but it’s very easy to take the best data in the world and make it look like it was shot with your old point-and-shoot camera.
An example: Stephanie Pahl Anderson
To help, I’d like to make an example of a wonderful processing job by Stephanie Pahl Anderson, who graciously provided the post-processing steps she performed in PixInsight to come up with her (the artist’s) version of my data.
The steps Stephanie performed in PixInsight:
- Debayer – Convert from monochrome raw to color, by interpreting the bayer matrix of the camera.
- Align – Register each image to each other so all the stars and details line up properly.
- Stack – Integrate and perform weighted averages of each pixel to increase the signal to noise ratio.
- Dynamic Background Extraction – Make the background of the image more uniform, fix vignetting.
- Background Neutralization – Equalize the color channels of the background of the image.
- Color Calibration – Fix inconsistency in the color channels by defining the white and black areas.
- TGVDenoise – Reduce noise intelligently.
- Masked Stretch – Stretch the histogram without blowing out the brighter areas, increasing the dynamic range.
- Local Histogram Equalization – Enhance the details in the areas of the image that show less contrast.
- Curves Transformation – Adjust color channels, saturation, and luminosity over a curve of the low-to-high brightness pixels.
- Morphological Transformation – Reduce star size to allow finer details to show through, enhance nebulae details.
- Subtractive Chromatic Noise Reduction – Reduce noise and protect color.
There is no wrong answer…most of the time
Once we make the cut from science to art, I don’t think there is a wrong answer, or interpretation, for an astronomical image. If you think it looks good — GREAT! You’ve made your art.
The cool thing is, because we all see it a little differently, have different processes, and have vastly differing skill levels, we all come up with different end results, even from the exact same input data.
To hammer the point home, just see the gallery below. Every image below started out from the data I made available in a previous article. Each image was post processed by different astrophotographers from around the globe. And just look at how different the results are.
Good job everyone, and stay tuned for more!