BGRem: A background noise remover for astronomical images based on a diffusion model
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
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2025
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| _version_ | 1866915999474778112 |
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| author | Nicolaas, Rodney Caron, Sascha Stoppa, Fiorenzo Bhattacharyya, Saptashwa de Austri, Roberto Ruiz Groot, Paul J. Levan, Andrew J. |
| author_facet | Nicolaas, Rodney Caron, Sascha Stoppa, Fiorenzo Bhattacharyya, Saptashwa de Austri, Roberto Ruiz Groot, Paul J. Levan, Andrew J. |
| contents | Context: Astronomical imaging aims to maximize signal capture while minimizing noise. Enhancing the signal-to-noise ratio directly on detectors is difficult and expensive, leading to extensive research in advanced post-processing techniques.
Aims: Removing background noise from images is a valuable pre-processing step catalog-building tasks. We introduce BGRem, a machine learning (ML) based tool to remove background noise from astronomical images.
Methods: BGRem uses a diffusion-based model with an attention U-Net as backbone, trained on simulated images for optical and gamma (γ)-ray data from the MeerLICHT and Fermi-LAT telescopes. In a supervised manner, BGRem learns to denoise astronomical images over several diffusion steps.
Results: BGRem performance was compared with a widely used tool for cataloging astronomical sources, SourceExtractor (SExtractor). It was shown that the amount of true positive sources using SExtractor increased by about 7% for MeerLICHT data when BGRem was used as a pre-processing step. We also show the generalizability of BGRem by testing it with optical images from different telescopes and also on simulated γ-ray data representative of the Fermi-LAT telescope. We show that in both cases, BGRem improves the source detection efficiency.
Conclusions: BGRem can improve the accuracy in source detection of traditional pixel-based methods by removing complex background noise. Using zero-shot approach, BGRem can generalize well to a wide range of optical images. The successful application of BGRem to simulated γ-ray images, alongside optical data, demonstrates its adaptability to distinct noise characteristics and observational domains. This cross-wavelength performance highlights its potential as a general-purpose background removal framework for multi-wavelength astronomical surveys. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_04718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BGRem: A background noise remover for astronomical images based on a diffusion model Nicolaas, Rodney Caron, Sascha Stoppa, Fiorenzo Bhattacharyya, Saptashwa de Austri, Roberto Ruiz Groot, Paul J. Levan, Andrew J. Instrumentation and Methods for Astrophysics Context: Astronomical imaging aims to maximize signal capture while minimizing noise. Enhancing the signal-to-noise ratio directly on detectors is difficult and expensive, leading to extensive research in advanced post-processing techniques. Aims: Removing background noise from images is a valuable pre-processing step catalog-building tasks. We introduce BGRem, a machine learning (ML) based tool to remove background noise from astronomical images. Methods: BGRem uses a diffusion-based model with an attention U-Net as backbone, trained on simulated images for optical and gamma (γ)-ray data from the MeerLICHT and Fermi-LAT telescopes. In a supervised manner, BGRem learns to denoise astronomical images over several diffusion steps. Results: BGRem performance was compared with a widely used tool for cataloging astronomical sources, SourceExtractor (SExtractor). It was shown that the amount of true positive sources using SExtractor increased by about 7% for MeerLICHT data when BGRem was used as a pre-processing step. We also show the generalizability of BGRem by testing it with optical images from different telescopes and also on simulated γ-ray data representative of the Fermi-LAT telescope. We show that in both cases, BGRem improves the source detection efficiency. Conclusions: BGRem can improve the accuracy in source detection of traditional pixel-based methods by removing complex background noise. Using zero-shot approach, BGRem can generalize well to a wide range of optical images. The successful application of BGRem to simulated γ-ray images, alongside optical data, demonstrates its adaptability to distinct noise characteristics and observational domains. This cross-wavelength performance highlights its potential as a general-purpose background removal framework for multi-wavelength astronomical surveys. |
| title | BGRem: A background noise remover for astronomical images based on a diffusion model |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2510.04718 |