# Night sky image processing – Part 4: Calculate the star centroid with sub-pixel accuracy

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 – the star centroid.

To keep it simple, in this example I apply the algorithm to calculate the star centroid only for one single star which I load from a FITS file. Mohammad Vali Arbabmir et. al.. proposed this algorithm in “Improving night sky star image processing algorithm for star sensors”.

Certainly it is possible to further optimize the implementation shown below. However, to me this 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 $x_{cog}$ (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 $x_{cog}$ and $y_{cog}$:

$$x_{cog} = \frac{\sum_{x=0}^{N-1}\sum_{y=0}^{M-1} x \cdot I(x,y) }{\sum_{x=0}^{N-1}\sum_{y=0}^{M-1}I(x,y)}$$

$$y_{cog} = \frac{\sum_{x=0}^{N-1}\sum_{y=0}^{M-1} y \cdot I(x,y) }{\sum_{x=0}^{N-1}\sum_{y=0}^{M-1}I(x,y)}$$

The only thing that changes between the two formulas is $x$ and $y$. The denominator just normalized the expression so that a valid position comes out. In addition, $N$ is the image width, $Y$ is the image height and $I(x,y)$ is the pixel value at position $(x,y)$.

Now, the only difference in the calculation of IWC is that each pixel value is additionally weighted with it’s own value: