ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution
cs.CV
Submitted: 2026-09-22
Updated: 2026-09-22
Comments: 4 pages of main text plus references, 4 figures. Submitted to ICASSP 2027
License: http://creativecommons.org/licenses/by/4.0/
The gist: Ground-based planetary imaging suffers from atmospheric turbulence, sensor noise, and limited sampling, making restoration a joint denoising, deblurring, and super-resolution problem.
Terminology
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 synthesized using measured layer-integrated turbulence strengths, propagated moving phase screens, exposure-averaged spatially varying PSFs, and sensor noise.ASTRA-SR first estimates a noise-suppressed but blur-retaining LR image, then restores spatial structure through multiscale processing and reconstructs HR detail with serial spatial-amplitude refinement. It yields a 0.49 dB foreground PSNR gain over the strongest baseline approaches.
Sources
- Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoising
- Training-Free Model Ensemble for Single-Image Super-Resolution via Strong-Branch Compensation
- Dual-Branch Remote Sensing Infrared Image Super-Resolution
- FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
- RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modeling
- Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
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