# On the segmentation of astronomical images via level-set methods

## TL;DR

This paper presents a two-step approach for segmenting noisy astronomical images by first enhancing image quality through intensity rescaling, then applying level-set methods for object identification, demonstrating effectiveness through various experiments.

## Contribution

It introduces a novel two-step process combining image enhancement and level-set segmentation specifically tailored for low-quality astronomical images.

## Key findings

- Enhanced segmentation accuracy on astronomical images.
- Effective use of different discretization techniques for level-set equations.
- Improved object detection in noisy, low-quality images.

## Abstract

Astronomical images are of crucial importance for astronomers since they contain a lot of information about celestial bodies that can not be directly accessible. Most of the information available for the analysis of these objects starts with sky explorations via telescopes and satellites. Unfortunately, the quality of astronomical images is usually very low with respect to other real images and this is due to technical and physical features related to their acquisition process. This increases the percentage of noise and makes more difficult to use directly standard segmentation methods on the original image. In this work we will describe how to process astronomical images in two steps: in the first step we improve the image quality by a rescaling of light intensity whereas in the second step we apply level-set methods to identify the objects. Several experiments will show the effectiveness of this procedure and the results obtained via various discretization techniques for level-set equations.