# URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration

arXiv: https://arxiv.org/abs/2505.23068 · HTML: https://mlsanity.com/paper/2505.23068/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement](https://mlsanity.com/paper/2303.06705/) (2023)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [LEDNet: Joint Low-light Enhancement and Deblurring in the Dark](https://mlsanity.com/paper/2202.03373/) (2022)
- [FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information](https://mlsanity.com/paper/2308.03033/) (2023)
- [Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement](https://mlsanity.com/paper/2405.03349/) (2024)

## Built on by (most cited first, 15 of 15)

- [SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2507.15520/) (2025)
- [ICLR: Inter-Chrominance and Luminance Interaction for Natural Color Restoration in Low-Light Image Enhancement](https://mlsanity.com/paper/2511.13607/) (2025)
- [Fourier-RWKV: A Multi-State Perception Network for Efficient Image Dehazing](https://mlsanity.com/paper/2512.08161/) (2025)
- [BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement](https://mlsanity.com/paper/2603.04975/) (2026)
- [PASDiff: Physics-Aware Semantic Guidance for Joint Real-World Low-Light Face Enhancement and Restoration](https://mlsanity.com/paper/2603.24969/) (2026)
- [Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework](https://mlsanity.com/paper/2603.25296/) (2026)
- [PixIE: Prompted Pixel-Space Low-Light Image Enhancement](https://mlsanity.com/paper/2605.23531/) (2026)
- [A Physically-Grounded Attack and Adaptive Defense Framework for Real-World Low-Light Image Enhancement](https://mlsanity.com/paper/2603.14304/) (2026)
- [DST-Net: A Dual-Stream Transformer with Illumination-Independent Feature Guidance and Multi-Scale Spatial Convolution for Low-Light Image Enhancement](https://mlsanity.com/paper/2603.16482/) (2026)
- [UHD Low-Light Image Enhancement via Real-Time Enhancement Methods with Clifford Information Fusion](https://mlsanity.com/paper/2604.09321/) (2026)
- [Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement](https://mlsanity.com/paper/2607.13925/) (2026)
- [CLUIE: Clustering-Aware Recurrent Propagation with Local Structural Compensation for Underwater Image Enhancement](https://mlsanity.com/paper/2607.21467/) (2026)
- [Beyond Illumination: A Conditional Mutual Information-Guided Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2608.01886/) (2026)
- [Compass: Degradation-Simulated Reciprocal Learning with Lightweight Needle RWKV for Multimodal Crack Segmentation under Missing Modalities](https://mlsanity.com/paper/2608.03559/) (2026)
- [Towards Color-Faithful Low-Light Image Enhancement via Adaptive Color Debiasing and Saturation Rectification](https://mlsanity.com/paper/2608.10512/) (2026)

## Abstract (arXiv)

Existing low-light image enhancement (LLIE) and joint LLIE and deblurring (LLIE-deblur) models have made strides in addressing predefined degradations, yet they are often constrained by dynamically coupled degradations. To address these challenges, we introduce a Unified Receptance Weighted Key Value (URWKV) model with multi-state perspective, enabling flexible and effective degradation restoration for low-light images. Specifically, we customize the core URWKV block to perceive and analyze complex degradations by leveraging multiple intra- and inter-stage states. First, inspired by the pupil mechanism in the human visual system, we propose Luminance-adaptive Normalization (LAN) that adjusts normalization parameters based on rich inter-stage states, allowing for adaptive, scene-aware luminance modulation. Second, we aggregate multiple intra-stage states through exponential moving average approach, effectively capturing subtle variations while mitigating information loss inherent in the single-state mechanism. To reduce the degradation effects commonly associated with conventional skip connections, we propose the State-aware Selective Fusion (SSF) module, which dynamically aligns and integrates multi-state features across encoder stages, selectively fusing contextual information. In comparison to state-of-the-art models, our URWKV model achieves superior performance on various benchmarks, while requiring significantly fewer parameters and computational resources.