In this post I want to show a way of Milkyway image processing which does not require any commercial software product. The idea is to use rawtherapee, DeepSkyStacker and GIMP to develop, align and combine the frames to one final image.
Camera: Fujifilm X-T1, Exposure time: 25sec. per frame, ISO: 1250, Aperture: f/2.8, focal length: 18mm
Quite often it is advantageous to use a high ISO value to get as much details as possible in the available exposure time (before the earth rotation becomes visible). On the other hand one probably does not want too much noise in the image. Therefore, the idea is to take multiple frames at a high ISO value and stack them later to reduce the noise. There is just one problem: The earth rotates and from frame to frame the stars are in different positions. Therefore an alignment of the frames before averaging is required. But then we get another problem: The foreground of each frame moves and so the resulting foreground gets fuzzy in the end. What to do? Continue reading →
In the parts 1-6 of my "Night sky image processing" Series I wrote about the different stages of night sky image processing. In this article I want to put all the pieces together and develop an "automatic star recognizer" which takes an astro-image as input and outputs a list of the recognized stars with their HFD and FWHM values.
Basically the processing pipeline is as follows:
ROI = Region of interest
In order to achieve this I use most of the concepts I have looked into earlier:
As input I used the following FITS image for testing. Continue reading →
In Part 5 of my "Night sky image processing" Series
I wrote about measuring the FWHM value of a star using curve fitting. Another measure for the star focus is the Half Flux Diameter (HFD). It was invented by Larry Weber
and Steve Brady
. The main two arguments for using the HFD is robustness and less computational effort compared to the FWHM approach
An article about the HFD can be found here
. Another short definition of the HFD I found here
. The original paper from Larry Weber
and Steve Bradley
can be found here
Let's start with the definition: "The HFD is defined as the diameter of a circle that is centered on the unfocused star image in which half of the total star flux is inside the circle and half is outside."
This definition can also be written in a mathematical fashion:
- is the pixel value minus the mean background value (!)
- is the distance from the centroid to each pixel
- is the number of pixels in the outer circle
- is the Half Flux Radius for which the sum becomes
I decided to put the formula here because it makes it easier to explain what is going on. In contrast to the definition in the other article I decided to name
Half Flux Radius (
) instead. This is because Continue reading →
In Part 3 of my "Night sky image processing" Series
I wrote about star-clustering. The result of this step was a list of stars and their pixels.
The next step is to find the center of each star. In this example the algorithm is only applied for one single star which is loaded from a FITS file for the sake of simplicity. The algorithm implemented here was proposed in "Improving night sky star image processing algorithm for star sensors"
from Mohammad Vali Arbabmir et. al..
The implementation could be further optimized but to me the code below is a good compromise between clarity and efficiency. The paper mentioned above will probably help a lot to get a better understanding of the implementation. Generally there are two steps:
Step 1: Calculation of the intensity weighted center (IWC) (to get an idea of where star center might be)
The calculation of IWC is based on the center of gravity CoG. The CoG is the same calculation as in physics but in this context just applied to an image. Each pixel brightness value has a weight - the brighter the "heavier". For example to get the "center" x position (this is the x position where the image has in x-direction the "brightest" value in mean. "In mean" means that all pixel values are considered). This directly leads to the following formulas for and :
The only thing that changes between the two formulas is Continue reading →
This article is about finding all the pixels which belong to a star - also known as star clustering. A short C++ implementation based on CImg is shown below. In Part 2 of my "Night sky image processing" Series
I was writing about thresholding. The result of the thresholding is a binary image. In this context it means the "1" pixels belong to stars, the "0" pixels belong to the background. I used Otsu's method to calculate an according threshold value.
The binary image is now further examined in a process called clustering
. In simple words the pixels which belong together (i.e. the neighbors) are grouped together. The result of this clustering process is a list of pixel groups which belong together - i.e. a list of stars and the pixels belonging to each star.
The implemented algorithm is based on the paper "Improving night sky star image processing algorithm for star sensors"
from Mohammad Vali Arbabmir et. al. However, in detail it is slightly different. My implementation assumes that the supplied image cannot be modified during the process. Hence I am using a set to put all white pixels into. To me this is a good compromise between memory consumption, performance and simplicity. However, the implementation can be further improved with respect to runtime performance when one would used a 2D array for the white pixels instead of a set.
The clustering algorithm is relatively straight forward:
- Collect all white pixels of the binary image to a bin
- Continue reading →