ASTRA-SR：用于天文图像超分辨率的大气视宁度与湍流恢复
ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution
- Hangzhou Dianzi University（杭州电子科技大学）
- Hong Kong University of Science and Technology, Guangzhou（香港科技大学（广州））
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ASTRA-SR是一种基于物理合成数据训练的盲单帧恢复框架，通过联合去噪、去模糊和超分辨率处理，在前景PSNR上比最强基线提升0.49 dB。
AI中文摘要：
地基行星成像受到大气湍流、传感器噪声和有限采样的影响，使得恢复成为一个联合去噪、去模糊和超分辨率问题。我们提出了ASTRA-SR，一种基于物理合成数据集训练的盲单帧恢复框架。高动态范围航天器RAW观测作为干净源，配对低分辨率输入通过使用测量的分层积分湍流强度、传播的移动相位屏、曝光平均的空间变化点扩散函数和传感器噪声合成。ASTRA-SR首先估计一个噪声抑制但保留模糊的低分辨率图像，然后通过多尺度处理恢复空间结构，并通过串行空间幅度细化重建高分辨率细节。与最强的基线方法相比，它在前景PSNR上获得了0.49 dB的提升。
英文摘要：
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.
