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Hubble Meets Webb: Image-to-Image Translation in Astronomy.

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Summary
This summary is machine-generated.

This study uses image-to-image translation to generate James Webb Space Telescope (JWST) images from Hubble Space Telescope (HST) data. Uncertainty estimation enhances translation reliability for astronomical planning.

Keywords:
denoising diffusion probabilistic modelsimage registrationimage-to-image translationsatellite image generationuncertainty estimation

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Area of Science:

  • Astronomy
  • Computer Vision
  • Machine Learning

Background:

  • Hubble Space Telescope (HST) and James Webb Space Telescope (JWST) provide crucial astronomical data.
  • Image-to-image translation techniques offer potential for generating synthetic JWST imagery from HST data.
  • Assessing image registration is critical for accurate astronomical data processing.

Purpose of the Study:

  • To explore image-to-image translation for generating JWST imagery from HST data.
  • To compare different translation methodologies including Pix2Pix, CycleGAN, TURBO, and DDPM-based Palette.
  • To introduce and evaluate a novel uncertainty quantification method for astronomical image translation.

Main Methods:

  • Comparative analysis of Pix2Pix, CycleGAN, TURBO, and DDPM-based Palette for sensor-to-sensor translation.
  • Integration of uncertainty estimation leveraging the stochastic nature of Denoising Diffusion Probabilistic Models (DDPMs).
  • Evaluation using metrics such as Mean Squared Error (MSE), Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), and Fréchet Inception Distance (FID).

Main Results:

  • Demonstrated the feasibility of generating JWST-like imagery from HST data using various image-to-image translation models.
  • Introduced a novel method for quantifying uncertainty in translated astronomical images.
  • The developed uncertainty estimation enhances the integrity of translated images, aiding astronomers in distinguishing reliable predictions.

Conclusions:

  • Image-to-image translation is a viable approach for astronomical sensor-to-sensor translation.
  • Uncertainty quantification is crucial for the reliable application of these techniques in astronomy.
  • The methodology provides predictive insights for planning JWST observations, optimizing resource allocation.