# Rethinking the Elementary Function Fusion for Single-Image Dehazing

arXiv: https://arxiv.org/abs/2405.15817 · HTML: https://mlsanity.com/paper/2405.15817/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [ParseNet: Looking Wider to See Better](https://mlsanity.com/paper/1506.04579/) (2015)
- [Densely Connected Pyramid Dehazing Network](https://mlsanity.com/paper/1803.08396/) (2018)
- [Gated Fusion Network for Single Image Dehazing](https://mlsanity.com/paper/1804.00213/) (2018)
- [O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images](https://mlsanity.com/paper/1804.05101/) (2018)
- [A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection](https://mlsanity.com/paper/1607.07155/) (2016)
- [From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data](https://mlsanity.com/paper/2108.02934/) (2021)

## Built on by (most cited first, 1 of 1)

- [Continual Learning-Based Unified Model for Unpaired Image Restoration Tasks](https://mlsanity.com/paper/2507.19184/) (2025)

## Abstract (arXiv)

This paper addresses the limitations of physical models in the current field of image dehazing by proposing an innovative dehazing network (CL2S). Building on the DM2F model, it identifies issues in its ablation experiments and replaces the original logarithmic function model with a trigonometric (sine) model. This substitution aims to better fit the complex and variable distribution of haze. The approach also integrates the atmospheric scattering model and other elementary functions to enhance dehazing performance. Experimental results demonstrate that CL2S achieves outstanding performance on multiple dehazing datasets, particularly in maintaining image details and color authenticity. Additionally, systematic ablation experiments supplementing DM2F validate the concerns raised about DM2F and confirm the necessity and effectiveness of the functional components in the proposed CL2S model. Our code is available at urlhttps://github.com/YesianRohn/CL2S, where the corresponding pre-trained models can also be accessed.