Using fractional differentiation in astronomy

TL;DR

This paper explores the application of fractional differential calculus to enhance and analyze astronomical images, improving the detection of faint objects and details for scientific research.

Contribution

It demonstrates how fractional differentiation can be used to produce content-matter based images, revealing faint astronomical features not easily seen with traditional methods.

Findings

Enhanced image quality and detail in astronomical images

Improved detection of faint objects and galactic matter

Potential for better scientific analysis of celestial images

Abstract

In a recent paper, published at arXiv:0910.2381, we started a discussion on the new possibilities arising from the use of fractional differential calculus in image processing. We have seen that the fractional calculation is able to enhance the quality of images, with interesting possibilities in edge detection and image restoration. Here, we want to discuss more deeply its role as a tool for the processing of astronomical images. In particular, the fractional differentiation can help produce a 'content-matter' based image from a pretty astronomical image that can be used for more research and scientific purposes, for instance to reveal faint objects galactic matter, nebulosity, more stars and planetary surface detail.