Best Practices

As expected, astrophotos are filled with stars. Hidden treasures often lie behind the starry brilliance. This is where the magic of StarNet in PixInsight is used to remove the stars that obscure the breathtaking details hiding in the background.

What if you could remove the stars? What would you be able to do?

The blurring effect within an image is often characterized by a mathematical function known as the Point Spread Function (PSF), which describes how a point source of light is spread out in the image. The point light sources are your stars, which should be circular.

When used with the PixInsight Deconvolution process, an accurate PSF model shows the blurring characteristics within the image, which deconvolution can correct. To be accurate, the PSF creation process should use a linear image (one that has not been stretched). When an image is stretched, the PSF characteristics are no longer accurate, resulting in a poor deconvolution result.

Many astrophotography workflows often recommend removing stars to simplify image processing. Without stars, you are able to focus your attention on the nebula and galaxies. However, the process of star removal can often remove some of the finer details within your image. This is why star removal is often completed early on in the astrophotography workflow.

But an interesting question often arises: Should I run deconvolution before or after I remove the stars?

As astrophotographers, we constantly strive to capture the beauty of the cosmos in our images, revealing the intricate details of distant galaxies, nebulae, and celestial wonders. But noise is an inevitable companion in astrophotography, often emerging as an unwanted pixels, marring the clarity and precision of our celestial images. Whether you are grappling with thermal noise, readout noise, or the notorious “grain” in your long-exposure shots, TGV Denoise (Total Generalized Variation Denoise) offers a solution to suppress the noise while highlighting the true beauty.

TGV Denoise employs a unique approach that goes beyond mere pixel-level processing. It takes into account the broader context of the image, understanding the relationships between pixels and preserving the fine details while effectively reducing noise.