Recently Browsing 0 members
No registered users viewing this page.
Startools 1.8 is currently under development, Ivo is currently working a Narrowband Accent" module for duo band users , initial image Ivo has posted certainly looks interesting https://forum.startools.org/viewtopic.php?f=4&t=2225&start=10 Ivo also working on a new deconvolution algorithm so some good things for Startools users to look forward too .
Hi SGL Hive Mind,
I’ve got a real head-scratcher of a problem, and I’m hoping someone here can help me solve it. I’ve been experimenting with seeing the effects of increasing integration time on background noise levels. My understanding is that the greater the total integration time, the smoother the background noise should appear. But I’m finding that beyond one hour of integration, my noise levels see no improvement, and even maintain the same general structure.
I flagged this in another thread but think it deserves its own thread, so I thought I’d begin anew.
I figure either my understanding of integration and noise is incorrect, or maybe I’ve messed up something in pre-processing. I’ve conducted a lot of tests with different settings, copied below, but nothing seems to make much difference. I’ve uploaded my data to GDrive, in case anyone’s feeling generous with their time, and would care to see if they get the repeated noise pattern! (Being GDrive, I think you need to be logged into a Google account to access).
My telescope is an Askar FRA400, and the camera is a 2600MC-Pro. All a series of 120-second images shot from Bortle 8 skies. For each test, I applied some basic functions in PixInsight just to get images to compare: ABE, ColorCalibration, EZ Stretch, Rescale to 1000px. I used SCNR to remove green from the first tests, but forgot that step for the second batch.
Any idea what's going on? Why isn't the noise smoothing out past the one hour mark?
Here are my PixInsight ImageIntegration settings:
I’m hoping a PixInsight guru can help me. I’m a PI beginner, but am having fun learning. My question is about the level of noise in my images. After integrating and performing an STF stretch, the resulting image always looks quite smooth. But it doesn’t take long at all – just a DBE really, maybe then a gentle stretch – for the image to become really noisy. And then a lot of my editing is centred on battling that noise. My camera is an ASI2600MC-Pro, which I cool to -10. For a recent experiment, I gathered 20 hours of data from 120s subs. With that much integration time, and the low-noise camera, I was hoping for lower noise than I actually got. (I am shooting from Bortle 8, however).
So my question is: are my expectations wrong, and actually the amount of noise I have is what’s to be expected? Or, have I messed something up in pre-processing or integration?
In case it’s useful, I ran SCRIPT -> ImageAnalysis -> Noise Evaluation on the image straight out of integration and got the following:
Ch | noise | count(%) | layers |
0 | 2.626e-01 | 18.39 | 4 |
1 | 1.037e-01 | 12.01 | 4 |
2 | 1.636e-01 | 11.10 | 4 |
I’ve also uploaded the file (1.16Gb) for anyone kind enough to help investigate further:
Recently I bought a ZWO ASI178MM for planetary/lunar/deep-sky imaging and last weekend i had clear skies so I was able to capture videos of the moon in two panels. I have already processed the videos in AutoStakkert, I used 3x drizzle because I intend to print the image so now I have two 120MB TIFF images that i would like to combine in a two panel mosaic. I tried doing it in Hugin, a free panorama stitcher but the program crashes due to the file sizes being too large. I have searched for tutorials on using DynamicAlignment in PixInsight but it seems to me that i'll need a reference image to be able to create mosaics in PixInsight. Is there any way to go about this, I feel like this should be a pretty straightforward job but I am not very experienced in PI so I would really appreciate some advice.
I tried using DynamicAlignment as you can see in the attached screenshot but the result was just a cut version of the target image, aligned perfectly to the source image. It would be perfect if it didn't cut off the lower part of the target image.