In astrophotography, no matter how much processing is done, digital images are still made up of numbers. To turn these numbers into images we can actually see, a step called image visualization is required.

Most screens and printed materials can only show 8-bit grayscale, which means 256 levels of brightness. The human eye also finds it hard to notice very small differences in brightness. In contrast, astronomical images often record a much wider range of light, usually more than 16 bits, where the brightest and darkest areas can differ by tens of thousands of times.

If 16-bit data is directly changed into 8-bit without adjustment, values from 0 to 511 in 16-bit data would be squeezed into just 0 or 1 in 8-bit. The result would be an image that looks almost completely black.

For scientific study, images must stay in their original linear data form. However, to make them visible to human eyes, non-linear adjustments, also called image stretching, are needed.

The simplest method is to multiply all brightness values by a fixed number, such as 10. This makes the image brighter, but very bright areas will quickly turn pure white because they exceed the maximum value that can be recorded. To avoid this problem, non-linear stretching is commonly used:

Dark areas are brightened more to reveal faint details.

Bright areas are adjusted less to avoid overexposure.

Although this process makes images easier to see, it changes the original brightness relationships. As a result, stretched images are no longer suitable for precise scientific measurement.

Color in astronomical images is also not a direct record of what the eye sees. Neither the Bayer filter in color cameras nor the RGB filters used with monochrome cameras can perfectly match how human eyes respond to different wavelengths of light. To highlight specific features in space, astrophotography often uses narrowband filters that allow only certain wavelengths to pass through, such as:

S-II、Hα、OIII 

These filters show the locations of different elements in clouds of gas. A famous example is the Hubble Space Telescope image Pillars of Creation, which combines S-II, Hα, and OIII images and assigns them to the red, green, and blue channels. Even though S-II and Hα both lie in the red part of the spectrum, assigning them different colors makes it easier to tell them apart. Images like this are called false-color images.

Many people look at astronomical images and ask, “Is this what a telescope really sees?”
The answer is no. These images go through long exposures, image stacking, stretching, and color mapping—not to copy human vision, but to reveal structures in the universe that our eyes cannot see.

Astronomical image visualization is a transformation from data to art, and from light to knowledge. It allows us not only to see the stars, but also to understand the forms and structures of the universe.