Problem
Research questions and friction points this paper is trying to address.
atmospheric turbulence
sensor noise
limited sampling
denoising
deblurring
ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution
🤖 AI Summary
针对地面行星成像受大气湍流、传感器噪声等问题，提出ASTRA-SR框架，通过物理合成数据集训练，实现联合去噪、去模糊和超分辨率处理。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Atmospheric Turbulence
Super-Resolution
Physics-Grounded Dataset
Multiscale Processing
Spatial-Amplitude Refinement
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