# NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression

arXiv: https://arxiv.org/abs/2312.06850 · HTML: https://mlsanity.com/paper/2312.06850/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 91 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 28 of 28)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [DehazeNet: An End-to-End System for Single Image Haze Removal](https://mlsanity.com/paper/1601.07661/) (2016)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [Contrastive Learning for Compact Single Image Dehazing](https://mlsanity.com/paper/2104.09367/) (2021)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [Gated Fusion Network for Single Image Dehazing](https://mlsanity.com/paper/1804.00213/) (2018)
- [Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline](https://mlsanity.com/paper/2004.01179/) (2020)
- [Domain Adaptation for Image Dehazing](https://mlsanity.com/paper/2005.04668/) (2020)
- [O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images](https://mlsanity.com/paper/1804.05101/) (2018)
- [Nighttime Dehazing with a Synthetic Benchmark](https://mlsanity.com/paper/2008.03864/) (2020)
- [Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data](https://mlsanity.com/paper/1904.08632/) (2019)
- [A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning](https://mlsanity.com/paper/2104.08902/) (2021)
- [Night Time Haze and Glow Removal using Deep Dilated Convolutional Network](https://mlsanity.com/paper/1902.00855/) (2019)

## Built on by (most cited first, 2 of 2)

- [Seeing Beyond Haze: Generative Nighttime Image Dehazing](https://mlsanity.com/paper/2503.08073/) (2025)
- [LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography](https://mlsanity.com/paper/2606.06901/) (2026)

## Abstract (arXiv)

This paper tackles the intricate challenge of improving the quality of nighttime images under hazy and low-light conditions. Overcoming issues including nonuniform illumination glows, texture blurring, glow effects, color distortion, noise disturbance, and overall, low light have proven daunting. Despite the inherent difficulties, this paper introduces a pioneering solution named Nighttime Dehazing, Low-Light Enhancement, and Light Suppression (NDELS). NDELS utilizes a unique network that combines three essential processes to enhance visibility, brighten low-light regions, and effectively suppress glare from bright light sources. In contrast to limited progress in nighttime dehazing, unlike its daytime counterpart, NDELS presents a comprehensive and innovative approach. The efficacy of NDELS is rigorously validated through extensive comparisons with eight state-of-the-art algorithms across four diverse datasets. Experimental results showcase the superior performance of our method, demonstrating its outperformance in terms of overall image quality, including color and edge enhancement. Quantitative (PSNR, SSIM) and qualitative metrics (CLIPIQA, MANIQA, TRES), measure these results.