Image Processing and Analysis of Multiple Wavelength Astronomical Data Using Python Tools

TL;DR

This paper presents a Python framework for processing and analyzing multi-wavelength astronomical images, enabling enhanced visualization and analysis of celestial data across visible spectrum bands.

Contribution

The paper introduces a comprehensive Python-based toolkit for astronomical image processing, including novel methods for multi-wavelength image blending and detailed pixel intensity analysis.

Findings

Effective noise reduction with median filtering

Enhanced image details through unsharp masking

Successful analysis of pixel intensity distributions across wavelengths

Abstract

We developed a Python based framework for astronomical image processing and analysis. Astronomical image loading, normalizing, stacking, and filtering processes represent visible range images from grayscale. Besides, the blending process helps to analyze the image of multiple wavelengths in the visible range. The methods take advantage of include median filtering for noise reduction, unsharp masking for sharpening details, and intensity normalization techniques. The detailed analysis of pixel intensity distributions and applying Gaussian fitting to variations across different wavelength bands. These methods highlight Python as a valuable tool for astronomers.