# HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement

arXiv: https://arxiv.org/abs/2504.02373 · HTML: https://mlsanity.com/paper/2504.02373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [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)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [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)
- [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022)
- [Towards Flexible Blind JPEG Artifacts Removal](https://mlsanity.com/paper/2109.14573/) (2021)
- [FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information](https://mlsanity.com/paper/2308.03033/) (2023)
- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [DarkIR: Robust Low-Light Image Restoration](https://mlsanity.com/paper/2412.13443/) (2024)
- [Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network](https://mlsanity.com/paper/2308.08220/) (2023)
- [CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.10362/) (2024)
- [Compressed Video Quality Enhancement with Temporal Group Alignment and Fusion](https://mlsanity.com/paper/2406.09693/) (2024)
- [Blind Video Super-Resolution based on Implicit Kernels](https://mlsanity.com/paper/2503.07856/) (2025)
- [OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal](https://mlsanity.com/paper/2408.11480/) (2024)
- [FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution](https://mlsanity.com/paper/2502.06431/) (2025)
- [CAPformer: Compression-Aware Pre-trained Transformer for Low-Light Image Enhancement](https://mlsanity.com/paper/2407.07056/) (2024)

## Abstract (arXiv)

In practical applications, low-light images are often compressed for efficient storage and transmission. Most existing methods disregard compression artifacts removal or hardly establish a unified framework for joint task enhancement of low-light images with varying compression qualities. To address this problem, we propose an efficient hybrid priors-guided network (HPGN) that enhances compressed low-light images by integrating both compression and illumination priors. Our approach fully utilizes the JPEG quality factor (QF) and DCT quantization matrix (QM) to guide the design of efficient plug-and-play modules for joint tasks. Additionally, we employ a random QF generation strategy to guide model training, enabling a single model to enhance low-light images with different compression levels. Experimental results demonstrate the superiority of our proposed method.