# Poisson Informed Retinex Network for Extreme Low-Light Image Enhancement

arXiv: https://arxiv.org/abs/2506.04470 · HTML: https://mlsanity.com/paper/2506.04470/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 67 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Generative Diffusion Prior for Unified Image Restoration and Enhancement](https://mlsanity.com/paper/2304.01247/) (2023)
- [Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement](https://mlsanity.com/paper/2110.00970/) (2021)
- [STAR: A Structure and Texture Aware Retinex Model](https://mlsanity.com/paper/1906.06690/) (2019)
- [Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network](https://mlsanity.com/paper/2012.15020/) (2020)
- [Semantically Contrastive Learning for Low-light Image Enhancement](https://mlsanity.com/paper/2112.06451/) (2021)
- [Towards Low Light Enhancement with RAW Images](https://mlsanity.com/paper/2112.14022/) (2021)
- [Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment](https://mlsanity.com/paper/2202.05972/) (2022)
- [Retinex Image Enhancement Based on Sequential Decomposition With a Plug-and-Play Framework](https://mlsanity.com/paper/2210.05436/) (2022)
- [NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network](https://mlsanity.com/paper/1909.05249/) (2019)
- [Learning a Single Convolutional Layer Model for Low Light Image Enhancement](https://mlsanity.com/paper/2305.14039/) (2023)
- [The Secrets of Non-Blind Poisson Deconvolution](https://mlsanity.com/paper/2309.03105/) (2023)
- [A fast and effective method for a Poisson denoising model with total variation](https://mlsanity.com/paper/1609.05035/) (2016)
- [Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework](https://mlsanity.com/paper/1803.00389/) (2018)

## Abstract (arXiv)

Low-light image denoising and enhancement are challenging, especially when traditional noise assumptions, such as Gaussian noise, do not hold in majority. In many real-world scenarios, such as low-light imaging, noise is signal-dependent and is better represented as Poisson noise. In this work, we address the problem of denoising images degraded by Poisson noise under extreme low-light conditions. We introduce a light-weight deep learning-based method that integrates Retinex based decomposition with Poisson denoising into a unified encoder-decoder network. The model simultaneously enhances illumination and suppresses noise by incorporating a Poisson denoising loss to address signal-dependent noise. Without prior requirement for reflectance and illumination, the network learns an effective decomposition process while ensuring consistent reflectance and smooth illumination without causing any form of color distortion. The experimental results demonstrate the effectiveness and practicality of the proposed low-light illumination enhancement method. Our method significantly improves visibility and brightness in low-light conditions, while preserving image structure and color constancy under ambient illumination.