The paper presents a generative AI approach that enhances galaxy images from ground‑based sky surveys to match the detail level of space‑based telescopes. By training on space‑based data, the model converts weak signals into clear, detailed galaxy images, enabling the combination of high survey throughput with superior image quality. The authors provide source code, training data, a catalog of 63,202 enhanced galaxy images, and a software tool that encapsulates the entire pipeline.
By Sai Teja Erukude, Lior Shamir
ASTRA-SR is a blind single‑frame restoration framework designed for ground‑based planetary imaging, addressing atmospheric turbulence, sensor noise, and limited sampling. It is trained on a physics‑grounded synthetic dataset that uses high‑dynamic‑range spacecraft RAW observations as clean sources and synthesizes low‑resolution inputs with realistic turbulence, PSFs, and noise. The method first produces a noise‑suppressed but blur‑retaining low‑resolution image, then restores spatial structure via multiscale processing and refines high‑resolution detail with serial spatial‑amplitude refinement, achieving a 0.49 dB PSNR gain over the strongest baseline approaches.
By Xining Ge, Ziteng Cui, Shuhong Liu
arXiv:2608.10343v2 Announce Type: replace
Abstract: While deep learning-based denoising has become widely adopted in low-dose CT, conventional models use generic architectures designed for natural im...
By Minwoo Yu, N. Robert Bennett, Jongduk Baek, Adam S. Wang
Raw images inherently suffer from noise due to the stochastic nature of light and sensor hardware imperfections. As real photon counts fall, the ratio of this noise to the signal degrades; consequently, for low-light conditions, robust denoising is especially vital for high-quality results.
arXiv:2604. 09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument.
By Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna, Jeroen Audenaert, V. Ashley Villar, David W. Hogg, Marc Huertas-Company, William T. Freeman
arXiv:2605. 29975v2 Announce Type: replace Abstract: We present a fully convolutional denoising autoencoder (FC-DAE) tailored for two-dimensional representations of dynamic correlations that is applicable to many experimental techniques.
By Nisar Nellikunnummel, Andi M Barbour, Lutz Wiegart, Tatiana Konstantinova, Anthony M DeGennaro
arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.
By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga
arXiv:2606. 10197v1 Announce Type: cross Abstract: Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution.
By Zehao Peng, Biprateep Dey, Chris J. Maddison, Joshua S. Speagle
The paper investigates learning a generative model for clean images from only noisy observations. It shows that Noise2Noise regression can learn posterior predictions at or above the training noise level, but to predict below that level a consistency constraint is required. The authors derive conditions under which this consistency uniquely determines the continuation to the clean image distribution and validate the approach on CIFAR-10, photon‑counting microscopy, and synthetic aperture radar data.
By Fabian Kruse, Lorenzo Baldassari, Ivan Dokmani\'c
arXiv:2609.35966v1 Announce Type: cross
Abstract: Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, compl...
By Shunyuan Mao, Andrea Isella, Paris Perdikaris, Li-Ta Lo, Hui Li
The paper presents a fully automated method for reconstructing the surface mass density of galaxy clusters using photometry and gravitational lensing data. It introduces DarkClusters-15k, a benchmark dataset of 15,000 simulated clusters with paired mass and photometry maps across multiple redshifts and simulation frameworks. By training a diffusion prior on this dataset, the authors generate posterior samples constrained by weak- and strong-lensing observables, achieving accurate, physics‑guided reconstructions with well‑calibrated uncertainties in minutes.
By Diego Royo, Brandon Zhao, Adolfo Mu\~noz, Diego Gutierrez, Katherine L. Bouman
The paper introduces a physics‑driven, cross‑domain iterative framework for self‑supervised low‑dose CT denoising. It first uses a learned sinogram prior and the LDCT noise model to separate Poisson and Gaussian noise components, then applies binomial and Gaussian data thinning to create two training pairs with independent noise realizations. These pairs train an image‑domain network whose outputs are forward‑projected to refine the prior, yielding consistent performance gains over existing self‑supervised baselines and comparable results to supervised methods.
By Xianlei Han, Shaoyu Wang, Jiancheng Fang, Weiwen Wu, Qiegen Liu