arXiv AI
Reinforcement learning for post-coronagraphic wavefront control
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On-sky demonstration of reinforcement learning for adaptive optics control
arXiv:2606. 10771v1 Announce Type: cross Abstract: Reinforcement learning (RL)-based algorithms have recently emerged as a promising approach for adaptive optics (AO) control.
arXiv AI
Jul 9
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks
arXiv:2607. 07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the physics and inverse problems that underlie computational imaging.
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A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
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Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
The paper introduces a deep‑learning pipeline that improves detection of faint moving objects in optical space situational awareness imagery. It combines a Tiny‑U‑Net segmentation network to mask stars with a partial‑convolution variational autoencoder (astro‑VAE) that learns background statistics and performs context‑aware inpainting. The reconstructed, star‑free backgrounds are used as a preprocessing step before detection, and when integrated with a shift‑and‑stack scheme, the method shows high‑fidelity background reconstruction and significant enhancement of moving‑target detectability in real ground‑based telescope data.
arXiv Computer Vision
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Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares
The paper introduces Semi-LAR, a semi‑supervised framework for removing nighttime lens flares. It uses an adaptive pseudo‑label repository that refines supervision through quality assessment, momentum updates, and invalid label filtering. A flare‑aware contrastive loss treats flare‑contaminated inputs as negatives, encouraging representations that distinguish flare patterns while aligning with reliable pseudo targets.
arXiv Machine Learning
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SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging
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