# Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement

arXiv: https://arxiv.org/abs/2408.12316 · HTML: https://mlsanity.com/paper/2408.12316/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [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)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Learning Blind Video Temporal Consistency](https://mlsanity.com/paper/1808.00449/) (2018)
- [Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications](https://mlsanity.com/paper/1605.01710/) (2016)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [Deep Generalized Unfolding Networks for Image Restoration](https://mlsanity.com/paper/2204.13348/) (2022)
- [Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems](https://mlsanity.com/paper/1704.03488/) (2017)
- [Iterative Prompt Learning for Unsupervised Backlit Image Enhancement](https://mlsanity.com/paper/2303.17569/) (2023)
- [UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments](https://mlsanity.com/paper/1904.04474/) (2019)
- [Denoising Prior Driven Deep Neural Network for Image Restoration](https://mlsanity.com/paper/1801.06756/) (2018)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement](https://mlsanity.com/paper/2110.00970/) (2021)
- [A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement](https://mlsanity.com/paper/1711.00591/) (2017)
- [PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement](https://mlsanity.com/paper/2210.00712/) (2022)
- [SALVE: Self-supervised Adaptive Low-light Video Enhancement](https://mlsanity.com/paper/2212.11484/) (2022)

## Built on by (most cited first, 7 of 7)

- [Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video Enhancement](https://mlsanity.com/paper/2503.11175/) (2025)
- [Generative Refocusing: Flexible Defocus Control from a Single Image](https://mlsanity.com/paper/2512.16923/) (2025)
- [Seeing the Unseen: Zooming in the Dark with Event Cameras](https://mlsanity.com/paper/2601.02206/) (2026)
- [Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques](https://mlsanity.com/paper/2507.08375/) (2025)
- [VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results](https://mlsanity.com/paper/2509.09190/) (2025)
- [Zero-Shot Video Restoration and Enhancement with Assistance of Video Diffusion Models](https://mlsanity.com/paper/2601.21922/) (2026)
- [Low-light Image Enhancement with Retinex Decomposition in Latent Space](https://mlsanity.com/paper/2603.15131/) (2026)

## Abstract (arXiv)

Obtaining pairs of low/normal-light videos, with motions, is more challenging than still images, which raises technical issues and poses the technical route of unpaired learning as a critical role. This paper makes endeavors in the direction of learning for low-light video enhancement without using paired ground truth. Compared to low-light image enhancement, enhancing low-light videos is more difficult due to the intertwined effects of noise, exposure, and contrast in the spatial domain, jointly with the need for temporal coherence. To address the above challenge, we propose the Unrolled Decomposed Unpaired Network (UDU-Net) for enhancing low-light videos by unrolling the optimization functions into a deep network to decompose the signal into spatial and temporal-related factors, which are updated iteratively. Firstly, we formulate low-light video enhancement as a Maximum A Posteriori estimation (MAP) problem with carefully designed spatial and temporal visual regularization. Then, via unrolling the problem, the optimization of the spatial and temporal constraints can be decomposed into different steps and updated in a stage-wise manner. From the spatial perspective, the designed Intra subnet leverages unpair prior information from expert photography retouched skills to adjust the statistical distribution. Additionally, we introduce a novel mechanism that integrates human perception feedback to guide network optimization, suppressing over/under-exposure conditions. Meanwhile, to address the issue from the temporal perspective, the designed Inter subnet fully exploits temporal cues in progressive optimization, which helps achieve improved temporal consistency in enhancement results. Consequently, the proposed method achieves superior performance to state-of-the-art methods in video illumination, noise suppression, and temporal consistency across outdoor and indoor scenes.