arXiv Machine Learning
Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
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arXiv AI
Aug 24
Amplifying the imaging power of digital sky surveys with space telescopes data and generative AI
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.
arXiv Computer Vision
Sep 23
ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution
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.
arXiv Computer Vision
Oct 15
Efficient Noise Context-Aware Representation for Low-Dose CT Denoising
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...
Hugging Face Trending Papers
Jul 13
Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising
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 Machine Learning
Jun 9
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
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.
arXiv Machine Learning
Aug 3
A Fully Convolutional Approach to Denoising 2D Correlation Spectra
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.