## Introduction
This will be a series of how I'm processing my astrophotos using PixInsight. My goal is to bring the basics of image processing to everyone who has started new in PixInsight and is a little bit confuse about it. I will also describe the different type of frames that are used in astrophotography and what they are used for. 
If someone wants to work through this series with me, there is a free evaluation version of PixInsight. You have to register on their website and you will get a key to trying it for 45 days. But unfortunately, I can not guarantee that I will be finished in 45 days. :-)
You can register here for the evaluation key: [https://pixinsight.com/trial/index.html](https://pixinsight.com/trial/index.html)
You also find more information about PixInsight on their website. [http://pixinsight.com](http://pixinsight.com)

## Bias frames or offset frames
Bias frames are calibration frames. Calibration frames are used to reduce disturbances from the images that contain the photographed object and to improve the quality of it. Every CCD or CMOS sensor produces different types of noise. With the bias frames, we try to eliminate the noise that is produced by the electronics of the sensor and the camera. 
Bias frames are made with the lowest possible exposure time of your camera. For DSLR this will be something around 1/4000 or 1/8000 second. Especially for my QHYCCD247C, it is 50μs. They are taken with the lens cap on your telescope or lens so that no light can be detected from the sensor. The ISO or Gain setting must correspond your other images and frame types. For example, if you have photographed your target with ISO 1600 than you have to choose also ISO 1600. If your camera/sensor can be cooled like mine use also the same temperature as for your other frames.

[![Darks und Bias_0000_-20C_2018-05-06_002844_0008.jpg](https://cdn.steemitimages.com/DQmRztTm2KL3NUuFeb4wpqdfwhFJwiU5HSskqngkMqdFUgM/Darks%20und%20Bias_0000_-20C_2018-05-06_002844_0008.jpg)](https://cdn.steemitimages.com/DQmRztTm2KL3NUuFeb4wpqdfwhFJwiU5HSskqngkMqdFUgM/Darks%20und%20Bias_0000_-20C_2018-05-06_002844_0008.jpg)

This is a bias frame straight out of the camera. As you see, you will see nothing. Let's open the image in PixInsigth. To see the information in it we need to stretch it. We use the auto stretch feature of the ScreenTransferFunction (PROCESS -> IntensityTransformations -> ScreenTransferFunction). This is the little radioactive sign. The ScreenTransferFunction will not alter any data of the image. It only changes how the image is displayed on your screen.

[![Bias_autostrech.jpg](https://cdn.steemitimages.com/DQmSdaYCH2Y7Wor5DMcL39dH9wXAv6SJjc77gRP31r7etVp/Bias_autostrech.jpg)](https://cdn.steemitimages.com/DQmSdaYCH2Y7Wor5DMcL39dH9wXAv6SJjc77gRP31r7etVp/Bias_autostrech.jpg)

Now we have our first bias frame. But it contains not just the noise that comes from the electronics, it also contains random noise from other interference sources. This can be free electrons or even despite cooling, thermal noise. Because we only want the noise that the camera produces while reading the sensor and digitalizing the data we need to find a solution to get rid of the random noise.

## Master bias
To get rid of the random noise we need many images that will be stacked. By stacking, only the static information is preserved while the random ones are lost in the mass of images. This final stacked frame will be called the master bias. To get a good representable master bias we need as many single frames as possible. Due to the short exposure time and the fact that you do not need to make new ones every time, it will be no problem to get a bunch of bias frames to work with.

I have stacked my master bias out of 256 bias frames. Less will probably work but the result won't maybe be as good. So the more bias frames that better.
To stack the separate bias frames to one master bias we are using the ImageIntegration process (PROCESS -> ImageIntegration -> ImageIntegration).
At first, we need to add all of our bias frames by clicking "Add Files".

[![add_files.jpg](https://cdn.steemitimages.com/DQmZ1JZHhReXsHGgn88AA5X95quv8s2rHaAddSm9VisyEnx/add_files.jpg)](https://cdn.steemitimages.com/DQmZ1JZHhReXsHGgn88AA5X95quv8s2rHaAddSm9VisyEnx/add_files.jpg)

If you have added all your single frames go to the "Image Integration" tab. Here we need to make some settings. For a basic stack that will remove the random noise use the following settings.
- Combination: Average
- Normalization: No normalization
- Weights: Don't care (all weights = 1)
- Check "Generate integrated image"

[![image_integration_settings.jpg](https://cdn.steemitimages.com/DQmUBeLfnCRsGhNNvYDm9AkkTFG2aGBy6XY2aUqtsx6APfm/image_integration_settings.jpg)](https://cdn.steemitimages.com/DQmUBeLfnCRsGhNNvYDm9AkkTFG2aGBy6XY2aUqtsx6APfm/image_integration_settings.jpg)

If you have set up everything like in the screenshot, go to the "Pixel Rejection (1)" tab. Here we use the following settings.
- Rejection algorithm: Linear Fit Clipping
- Normalization: No normalization
- Check "Clip low pixels"
- Check "Clip high pixels"

As we are using so much bias frames we will choose "Linear Fit Clipping" as algorithm. As a small clue, you can use "Winsorized Sigma Clipping" <=20 images and "Linear Fit Clipping" if you have >20 images.

[![pixel_rejection.jpg](https://cdn.steemitimages.com/DQmQ5MaCWWmdWZT3BTocx8BSZLTRjR9qKaCUUvUSsBW9eEy/pixel_rejection.jpg)](https://cdn.steemitimages.com/DQmQ5MaCWWmdWZT3BTocx8BSZLTRjR9qKaCUUvUSsBW9eEy/pixel_rejection.jpg)

Have you everything set up? Then we go to the "Pixel Rejection (2)" tab. Here we will leave the default settings.

[![pixel_rejection_2.jpg](https://cdn.steemitimages.com/DQmZA1kwPQ8cbjbGzbBaBLm1qm6UtBVi7FXpTZhTschJbdu/pixel_rejection_2.jpg)](https://cdn.steemitimages.com/DQmZA1kwPQ8cbjbGzbBaBLm1qm6UtBVi7FXpTZhTschJbdu/pixel_rejection_2.jpg)

Once everything is set up and done, we click the small circle (Apply Global) at the bottom of the ImageIntegration window. This will start the stacking process. The process console will open and show you the actual status of the process. Stacking can take a while it depends on the hardware of your computer. So, drink a coffee or do something else while your master bias gets stacked. On my old Notebook, this took around 2 hours, my new one is ready in 20 minutes with it.

[![process_console.png](https://cdn.steemitimages.com/DQmXetfNZinHjzubFi3UZe48UyKCfjTdzyk3qKocTTq3FHo/process_console.png)](https://cdn.steemitimages.com/DQmXetfNZinHjzubFi3UZe48UyKCfjTdzyk3qKocTTq3FHo/process_console.png)

After the process is finished a new image is opened. First safe this as "masterbias". Make sure you use the PixInsight file format ".xisf".
After an auto stretch with the ScreenTransferFunktion you should see the data in the master bias. 

[![masterbias_autostretch.jpg](https://cdn.steemitimages.com/DQmTcC8V8AGwgRhTewT8Xt6e5Ws4Lt2JbtvViNph2y9RCRk/masterbias_autostretch.jpg)](https://cdn.steemitimages.com/DQmTcC8V8AGwgRhTewT8Xt6e5Ws4Lt2JbtvViNph2y9RCRk/masterbias_autostretch.jpg)

Let's compare a single bias frame with the stacked master bias.

[![compare_bias_masterbias.jpg](https://cdn.steemitimages.com/DQmWhHgwbY5CXj8MwhpA6CtHnVLioS5thuTVeJ1qpSWxYeC/compare_bias_masterbias.jpg)](https://cdn.steemitimages.com/DQmWhHgwbY5CXj8MwhpA6CtHnVLioS5thuTVeJ1qpSWxYeC/compare_bias_masterbias.jpg)

On the left is the single bias frame and on the right is the master bias. You see that the master bias has a significantly lower noise. The master bias has lost almost the most of the random noise. The noise that left is the noise that appears due to the sensor readout and the electronics.

## Conclusion
We now have the first type of calibration frame. With the master bias, we can subtract the disturbance caused by our camera and its electronics from our images. This will give us an improvement of quality in the final image. And we want to get the best possible quality in the final image. :-)


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**Thank you very much for visiting and reading!**
I hope that this has been a useful article for you. If you have any questions or suggestions please don't be afraid to let me know of anything you thought about this post!


Yours, 
[@astrophoto.kevin](https://steemit.com/@astrophoto.kevin)
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All images, otherwise clearly indicated, in this post are my own work.
You can use it for free if you credit them to [@astrophoto.kevin](https://steemit.com/@astrophoto.kevin)


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