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Transformer-Based Restoration: Quantitative Gains and Boundaries in Space Data

7 May 2025

Transformer-based AI boosts HST images to JWST quality, enhancing detail and accuracy for astronomy-despite challenges with noise and point sources.

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Even AI Needs Glasses: When Space Images Get Too Fuzzy to Fix

7 May 2025

Restoration falters at high noise, on stellar point sources, and with correlated artifacts-highlighting key limits of current deep learning for astronomy images

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AI Brings HST Images Closer to JWST Quality with Restormer

7 May 2025

Applying Restormer to real HST images yields clearer, deeper, less noisy galaxies, with measurable gains despite GT data’s absence for direct comparison.

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Quantifying Restoration: PSNR, SSIM, and Morphology in Deep-Learned Galaxies

7 May 2025

Restormer restores galaxy images with sharper detail, improved morphology, and superior photometry, outperforming degraded inputs by wide margins.

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Bridging Simulations and Observations: GalSim & JWST Data Drive Deep Learning

6 May 2025

GalSim generates realistic pre-training data; JWST galaxies provide high-fidelity fine-tuning, enabling robust transformer-based enhancement of space images.

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Space Selfies Level Up-AI Learns from the Best in the Universe

6 May 2025

Creates paired HST-JWST galaxy images for training, using synthetic and real data to teach Transformers superior astronomical image restoration.

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Efficient Transformers for Astronomical Images: Deconvolution and Denoising Unleashed

6 May 2025

Restormer’s transformer architecture powerfully restores HST images, combining deconvolution and denoising for JWST-level clarity using transfer learning.

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AI Breakthrough Sharpens Telescope Images-Astronomy’s Next Big Leap

6 May 2025

Applies efficient Transformers to restore and enhance astronomical images, matching JWST quality and outperforming traditional methods in precision.

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New Open-Source Platform Is Letting AI Researchers Crack Tough Languages

30 Dec 2024

Researchers in Poland have developed an open-source tool that improves the evaluation and comparison of AI used in natural language preprocessing.