jpeg2fits: Convert JPEG Images to FITS in Seconds

From JPEG to FITS: A Beginner’s Guide to jpeg2fits

What this guide covers

• Purpose: Convert standard JPEG images into FITS (Flexible Image Transport System) files commonly used in astronomy and scientific imaging.
• Audience: Beginners with basic command-line familiarity who want a quick, reliable conversion tool.
• Outcome: Produce FITS files preserving pixel values, add basic metadata, and prepare images for scientific analysis.

Why convert JPEG → FITS

• FITS is standard for astronomical data and supports multidimensional arrays and extensive metadata.
• Metadata support: Store observation details (date, instrument, exposure) in header keywords.
• Analysis-ready: FITS works with astronomy tools (ds9, Astropy, IRAF) while JPEG does not.

Typical workflow (assumes jpeg2fits command-line tool)

1. Install jpeg2fits

   • Common methods: pip, prebuilt binaries, or package manager (assume pip if Python-based).
   • Example: pip install jpeg2fits (replace with actual install command for your distro).

2. Basic conversion

   • Command: jpeg2fits input.jpg output.fits
   • Result: single-extension FITS file with image data in primary HDU.

3. Preserve or set metadata

   • Add header keywords: jpeg2fits input.jpg output.fits –add KEY=VALUE
   • Common keys: DATE-OBS, INSTRUME, TELESCOP, EXPTIME, COMMENT.

4. Batch conversion

   • Example (bash): for f in.jpg; do jpeg2fits “\(f" "\){f%.jpg}.fits”; done

5. Scaling and datatype

   • Options to control scaling (normalize, stretch) and output datatype (e.g., 16-bit int, float).
   • Example flags: –scale none or –dtype float32 (tool-specific).

6. Verify and view

   • Inspect header: fitsinfo output.fits or python -c “from astropy.io import fits; print(fits.open(‘output.fits’)[0].header)”
   • View image: ds9 output.fits or matplotlib in Python.

Example using Python + Astropy (if jpeg2fits unavailable)

• Read JPEG, convert to FITS, add header, save:

python
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from PIL import Image import numpy as np from astropy.io import fits img = Image.open(‘input.jpg’).convert(‘L’)  # grayscale; use ‘RGB’ to keep channels
data = np.array(img)
hdu = fits.PrimaryHDU(data.astype(‘uint16’))
hdu.header[‘COMMENT’] = ‘Converted from JPEG on 2026-02-05’
hdu.writeto(‘output.fits’, overwrite=True)

Tips & caveats

• JPEG is lossy: original pixel values are approximated; not ideal for precision photometry.
• For scientific use, prefer RAW, TIFF, or native instrument formats when available.
• Keep a record of any scaling applied during conversion in the FITS header.

Further reading / next steps

• Learn FITS header conventions (FITS Standard).
• Use Astropy for advanced header editing and multi-extension FITS.
• Integrate conversion into preprocessing pipelines for stacking, calibration, or photometry.

(Date: 2026-02-05)