# Nighttime Dehazing with a Synthetic Benchmark

arXiv: https://arxiv.org/abs/2008.03864 · HTML: https://mlsanity.com/paper/2008.03864/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 30 of 41)

- [Flare7K: A Phenomenological Nighttime Flare Removal Dataset](https://mlsanity.com/paper/2210.06570/) (2022)
- [Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution](https://mlsanity.com/paper/2308.01738/) (2023)
- [Flare7K++: Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond](https://mlsanity.com/paper/2306.04236/) (2023)
- [NightHaze: Nighttime Image Dehazing via Self-Prior Learning](https://mlsanity.com/paper/2403.07408/) (2024)
- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer](https://mlsanity.com/paper/2305.09533/) (2023)
- [Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning](https://mlsanity.com/paper/2212.11473/) (2022)
- [MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal](https://mlsanity.com/paper/2406.18079/) (2024)
- [Let Segment Anything Help Image Dehaze](https://mlsanity.com/paper/2306.15870/) (2023)
- [A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth](https://mlsanity.com/paper/2206.06427/) (2022)
- [SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving](https://mlsanity.com/paper/2403.17094/) (2024)
- [Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing](https://mlsanity.com/paper/2109.07100/) (2021)
- [RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image Dehazing](https://mlsanity.com/paper/2405.09083/) (2024)
- [CoA: Towards Real Image Dehazing via Compression-and-Adaptation](https://mlsanity.com/paper/2504.05590/) (2025)
- [Bilevel Layer-Positioning LoRA for Real Image Dehazing](https://mlsanity.com/paper/2603.10872/) (2026)
- [From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement](https://mlsanity.com/paper/2307.16783/) (2023)
- [DGSolver: Diffusion Generalist Solver with Universal Posterior Sampling for Image Restoration](https://mlsanity.com/paper/2504.21487/) (2025)
- [Rethinking Nighttime Image Deraining via Learnable Color Space Transformation](https://mlsanity.com/paper/2510.17440/) (2025)
- [UniSER: A Foundation Model for Unified Soft Effects Removal](https://mlsanity.com/paper/2511.14183/) (2025)
- [Reliable Image Dehazing by NeRF](https://mlsanity.com/paper/2303.09153/) (2023)
- [Emphasizing Crucial Features for Efficient Image Restoration](https://mlsanity.com/paper/2405.11468/) (2024)
- [Seeing Beyond Haze: Generative Nighttime Image Dehazing](https://mlsanity.com/paper/2503.08073/) (2025)
- [SmokeBench: A Real-World Dataset for Surveillance Image Desmoking in Early-Stage Fire Scenes](https://mlsanity.com/paper/2509.12701/) (2025)
- [A comprehensive benchmark analysis for sand dust image reconstruction](https://mlsanity.com/paper/2202.03031/) (2022)
- [Harmonizing Light and Darkness: A Symphony of Prior-guided Data Synthesis and Adaptive Focus for Nighttime Flare Removal](https://mlsanity.com/paper/2404.00313/) (2024)
- [UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing](https://mlsanity.com/paper/2601.06909/) (2026)
- [NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression](https://mlsanity.com/paper/2312.06850/) (2023)
- [Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration](https://mlsanity.com/paper/2505.16479/) (2025)
- [SAR-to-Optical Image Translation via Thermodynamics-inspired Network](https://mlsanity.com/paper/2305.13839/) (2023)
- [On the Global Photometric Alignment for Low-Level Vision](https://mlsanity.com/paper/2604.08172/) (2026)

## Abstract (arXiv)

Increasing the visibility of nighttime hazy images is challenging because of uneven illumination from active artificial light sources and haze absorbing/scattering. The absence of large-scale benchmark datasets hampers progress in this area. To address this issue, we propose a novel synthetic method called 3R to simulate nighttime hazy images from daytime clear images, which first reconstructs the scene geometry, then simulates the light rays and object reflectance, and finally renders the haze effects. Based on it, we generate realistic nighttime hazy images by sampling real-world light colors from a prior empirical distribution. Experiments on the synthetic benchmark show that the degrading factors jointly reduce the image quality. To address this issue, we propose an optimal-scale maximum reflectance prior to disentangle the color correction from haze removal and address them sequentially. Besides, we also devise a simple but effective learning-based baseline which has an encoder-decoder structure based on the MobileNet-v2 backbone. Experiment results demonstrate their superiority over state-of-the-art methods in terms of both image quality and runtime. Both the dataset and source code will be available at https://github.com/chaimi2013/3R.