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ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution

Paper recorded by Signals 4 on 2026-09-22 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23

Category: cs.CV · 计算机视觉 · first seen 2026-09-23

Abstract

Ground-based planetary imaging suffers from atmospheric turbulence, sensor noise, and limited sampling, making restoration a joint denoising, deblurring, and super-resolution problem. We present ASTRA-SR, a blind single-frame restoration framework trained on a physics-grounded synthetic dataset. High-dynamic-range spacecraft RAW observations serve as clean sources, and paired LR inputs are synthes

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#21 most recent of 301 cs.CV papers we have recorded · ↑ newer: Evaluating the Semantic-to-Geometric Gap in Adversarial Defenses Again · ↓ older: GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Dis
Cite this page: ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution: the #21 most recent of 301 cs.CV papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/astra-sr-atmospheric-seeing-and-turbulence-restoration-for-astronomical-image-su.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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