mlsanityData to 14 Oct 2026

Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement

arXiv 2501.14265 ↗First posted 24 Jan 2025cs.CVcited by 4

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Abstract (arXiv)

In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images due to dynamic photography conditions. This naturally results in a one-to-many mapping problem. To address this, we propose a Bayesian Enhancement Model (BEM) that incorporates Bayesian Neural Networks (BNNs) to capture data uncertainty and produce diverse outputs. To enable fast inference, we introduce a BNN-DNN framework: a BNN is first employed to model the one-to-many mapping in a low-dimensional space, followed by a Deterministic Neural Network (DNN) that refines fine-grained image details. Extensive experiments on multiple low-light and underwater image enhancement benchmarks demonstrate the effectiveness of our method.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 25 of its 46 references that are in the map. First posted on arXiv 24 Jan 2025.

Builds on

25 of 46 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  5. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  6. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  7. 2018Deep Retinex Decomposition for Low-Light Enhancementcited by 723
  8. 2020Zero-Reference Deep Curve Estimation for Low-Light Image Enhancementcited by 542
  9. 2019EnlightenGAN: Deep Light Enhancement without Paired Supervisioncited by 466
  10. 2019Kindling the Darkness: A Practical Low-light Image Enhancercited by 355
  11. 2020Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancementcited by 303
  12. 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 253
13 more
  1. 2021Low-Light Image Enhancement with Normalizing Flowcited by 185
  2. 2019Fast Underwater Image Enhancement for Improved Visual Perceptioncited by 183
  3. 2020Efficient and Scalable Bayesian Neural Nets with Rank-1 Factorscited by 171
  4. 2015Modelling Uncertainty in Deep Learning for Camera Relocalizationcited by 134
  5. 2021Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embeddingcited by 134
  6. 2021R2RNet: Low-light Image Enhancement via Real-low to Real-normal Networkcited by 106
  7. 2023Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bankcited by 71
  8. 2023PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminatorscited by 49
  9. 2023Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategycited by 33
  10. 2024GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrievalcited by 33
  11. 2019Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayescited by 32
  12. 2021Single Underwater Image Restoration by Contrastive Learningcited by 11
  13. 2024Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifiercited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025From Restoration to Reconstruction: Rethinking 3D Gaussian Splatting for Underwater Scenescited by 2
  2. 2025NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Resultscited by 1
  3. 2026PixIE: Prompted Pixel-Space Low-Light Image Enhancementcited by 1
  4. 2025Marine Snow Removal Using Internally Generated Pseudo Ground Truthcited by 0