Automated Color Astronomical Image Processing is a project based on Python and OpenCV, aimed at automating the processing of astronomical images by integrating relevant FITS file processing libraries. The project has successfully implemented various functions, including:
- Image calibration: ensures that the observed images match the actual celestial positions, enhancing the accuracy and reliability of the images.
- Noise reduction: techniques such as Gaussian noise reduction, mean noise reduction, and bilateral noise reduction effectively reduce noise in the images, improving the clarity of observational results.
- Alignment: aligns multiple images to eliminate image distortion and facilitate subsequent stacking.
- Stacking: combines multiple images to enhance the signal, making faint celestial objects easier to observe.
- Debayering: converts the original Bayer format images to RGB format images, restores image details, and improves the realism and clarity of the images.
- Automatic stretching: adjusts the image contrast to highlight details, providing more valuable data for subsequent scientific analysis.
- Automated processing: integrates the above functions to achieve one-click processing of color and black-and-white astronomical images, including noise reduction, calibration, alignment, debayering, stacking, and automatic stretching.
Features:
- Supports color and black-and-white astronomical image processing
- One-click processing, no manual intervention required
- Covers functions such as noise reduction, calibration, alignment, debayering, stacking, and automatic stretching
- Easy to use
Application scenarios:
- Astronomy enthusiasts: used to process and share observation results
- Scientific research institutions: used for scientific research of astronomical images
How to use:
- Install Python 3 and OpenCV library
- Download the project code
- Run the project code
- Select the image file to be processed
- Select the processing mode (color or black and white)
- Click the "Start" button to complete the automated processing
Contact information:
- Email: fenghao3737@proton.me