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LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement

arXiv 1511.03995 ↗First posted 12 Nov 2015cs.CVcited by 224

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Abstract (arXiv)

In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredients to making informed decisions which determines the success of an operation. Camera sensors are often cost-limited in ability to clearly capture objects without defects from images or videos taken in a poorly-lit environment. The goal in many applications is to enhance the brightness, contrast and reduce noise content of such images in an on-board real-time manner. We propose a deep autoencoder-based approach to identify signal features from low-light images handcrafting and adaptively brighten images without over-amplifying the lighter parts in images (i.e., without saturation of image pixels) in high dynamic range. We show that a variant of the recently proposed stacked-sparse denoising autoencoder can learn to adaptively enhance and denoise from synthetically darkened and noisy training examples. The network can then be successfully applied to naturally low-light environment and/or hardware degraded images. Results show significant credibility of deep learning based approaches both visually and by quantitative comparison with various popular enhancing, state-of-the-art denoising and hybrid enhancing-denoising techniques.

Key facts. Cited by 224 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 43; 2022: 44; 2023: 48; 2024: 30; 2025: 36; 2026: 23.

224 papers.

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  1. 2023Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancementcited by 231
  2. 2023Generative Diffusion Prior for Unified Image Restoration and Enhancementcited by 199
  3. 2021Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimationcited by 192
  4. 2021Low-Light Image Enhancement with Normalizing Flowcited by 185
  5. 2022Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Methodcited by 174
  6. 2022Learning Enriched Features for Fast Image Restoration and Enhancementcited by 150
  7. 2021Low-Light Image and Video Enhancement Using Deep Learning: A Surveycited by 148
  8. 2023Low-Light Image Enhancement with Wavelet-based Diffusion Modelscited by 127
  9. 2023Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Modelcited by 118
  10. 2021R2RNet: Low-light Image Enhancement via Real-low to Real-normal Networkcited by 106
  11. 2022LEDNet: Joint Low-light Enhancement and Deblurring in the Darkcited by 96
  12. 2023Implicit Neural Representation for Cooperative Low-light Image Enhancementcited by 92
38 more
  1. 2022You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correctioncited by 81
  2. 2022Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppressioncited by 73
  3. 2024LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Modelscited by 71
  4. 2023Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Modelcited by 69
  5. 2023Pyramid Diffusion Models For Low-light Image Enhancementcited by 67
  6. 2021Low-light Image Enhancement via Breaking Down the Darknesscited by 65
  7. 2023FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Informationcited by 64
  8. 2021Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancementcited by 60
  9. 2022Multitask AET with Orthogonal Tangent Regularity for Dark Object Detectioncited by 59
  10. 2024Retinexmamba: Retinex-based Mamba for Low-light Image Enhancementcited by 59
  11. 2024MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Spacecited by 48
  12. 2023Instance Segmentation in the Darkcited by 46
  13. 2024GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrievalcited by 33
  14. 2021StarEnhancer: Learning Real-Time and Style-Aware Image Enhancementcited by 32
  15. 2021Semantically Contrastive Learning for Low-light Image Enhancementcited by 31
  16. 2023CLE Diffusion: Controllable Light Enhancement Diffusion Modelcited by 30
  17. 2023ExposureDiffusion: Learning to Expose for Low-light Image Enhancementcited by 29
  18. 2022DocEnTr: An End-to-End Document Image Enhancement Transformercited by 28
  19. 2023Human Pose Estimation in Extremely Low-Light Conditionscited by 27
  20. 2021ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancementcited by 26
  21. 2021Towards Low Light Enhancement with RAW Imagescited by 26
  22. 2024You Only Need One Color Space: An Efficient Network for Low-light Image Enhancementcited by 24
  23. 2021Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancementcited by 23
  24. 2021Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Visioncited by 22
  25. 2024Towards Real-world Event-guided Low-light Video Enhancement and Deblurringcited by 22
  26. 2022Learning to Adapt to Lightcited by 21
  27. 2024Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representationscited by 21
  28. 2022An Annotation-free Restoration Network for Cataractous Fundus Imagescited by 20
  29. 2023Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognitioncited by 20
  30. 2024Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusioncited by 19
  31. 2022Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyondcited by 17
  32. 2022Structure-consistent Restoration Network for Cataract Fundus Image Enhancementcited by 16
  33. 2024AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancementcited by 16
  34. 2021Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilationcited by 15
  35. 2023LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusioncited by 15
  36. 2023LLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancementcited by 15
  37. 2021UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Datacited by 14
  38. 2022HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Trackingcited by 14

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