mlsanityData to 7 Oct 2026

Learning to Kindle the Starlight

arXiv 2211.09206 ↗First posted 16 Nov 2022cs.CV eess.IVcited by 1

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Capturing highly appreciated star field images is extremely challenging due to light pollution, the requirements of specialized hardware, and the high level of photographic skills needed. Deep learning-based techniques have achieved remarkable results in low-light image enhancement (LLIE) but have not been widely applied to star field image enhancement due to the lack of training data. To address this problem, we construct the first Star Field Image Enhancement Benchmark (SFIEB) that contains 355 real-shot and 854 semi-synthetic star field images, all having the corresponding reference images. Using the presented dataset, we propose the first star field image enhancement approach, namely StarDiffusion, based on conditional denoising diffusion probabilistic models (DDPM). We introduce dynamic stochastic corruptions to the inputs of conditional DDPM to improve the performance and generalization of the network on our small-scale dataset. Experiments show promising results of our method, which outperforms state-of-the-art low-light image enhancement algorithms. The dataset and codes will be open-sourced.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 36 of its 46 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

36 of 46 references.

Closest ancestors · 4

Same direction, newest first

  1. 2022AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models24 Aug
  2. 2022Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models29 Jul
  3. 2022Denoising Diffusion Restoration Models27 Jan
  4. 2021Realistic galaxy image simulation via score-based generative models2 Nov

Other references · 32

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  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  7. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  8. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
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  12. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
20 more
  1. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  2. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  3. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  4. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  5. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  6. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  7. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
  8. 2018Deep Retinex Decomposition for Low-Light Enhancementcited by 720
  9. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  10. 2018Albumentations: fast and flexible image augmentationscited by 558
  11. 2020Zero-Reference Deep Curve Estimation for Low-Light Image Enhancementcited by 541
  12. 2019EnlightenGAN: Deep Light Enhancement without Paired Supervisioncited by 466
  13. 2018Learning to See in the Darkcited by 459
  14. 2019Kindling the Darkness: A Practical Low-light Image Enhancercited by 355
  15. 2015LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancementcited by 224
  16. 2021Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimationcited by 192
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  19. 2022High-Resolution Image Editing via Multi-Stage Blended Diffusioncited by 14
  20. 2021The Simulated Sky: Stellarium for Cultural Astronomy Researchcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognitioncited by 20

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  1. 2025Diffusion Models for Low-Light Image Enhancement: A Multi-Perspective Taxonomy and Performance Analysis
  2. 2023LLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancement
  3. 2024Zero-Shot Low Light Image Enhancement with Diffusion Prior
  4. 2024Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement
  5. 2024Low-Light Image Enhancement via Generative Perceptual Priors
  6. 2025DarkDiff: Advancing Low-Light Raw Enhancement by Retasking Diffusion Models for Camera ISP
  7. 2023LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
  8. 2024Super-resolving Real-world Image Illumination Enhancement: A New Dataset and A Conditional Diffusion Model