# Single Image Dehazing Using Ranking Convolutional Neural Network

arXiv: https://arxiv.org/abs/2001.05246 · HTML: https://mlsanity.com/paper/2001.05246/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data](https://mlsanity.com/paper/2108.02934/) (2021)
- [Learning for Unconstrained Space-Time Video Super-Resolution](https://mlsanity.com/paper/2102.13011/) (2021)
- [Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning](https://mlsanity.com/paper/2212.11473/) (2022)
- [WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal](https://mlsanity.com/paper/2303.13739/) (2023)
- [T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing](https://mlsanity.com/paper/2106.02809/) (2021)
- [UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement](https://mlsanity.com/paper/2306.08344/) (2023)
- [A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation](https://mlsanity.com/paper/2203.04812/) (2022)
- [Single Image Dehazing Using Scene Depth Ordering](https://mlsanity.com/paper/2408.05683/) (2024)
- [A GAN-Based Input-Size Flexibility Model for Single Image Dehazing](https://mlsanity.com/paper/2102.09796/) (2021)
- [Analysis of Multiscale Wavelet-based Fractional Gradient-Anisotropic Diffusion Fusion for single hazy and underwater image enhancement](https://mlsanity.com/paper/2111.15479/) (2021)
- [See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images](https://mlsanity.com/paper/2210.07594/) (2022)
- [Synesthesia of Machines (SoM)-Enhanced Sub-THz ISAC Transmission for Air-Ground Network](https://mlsanity.com/paper/2506.12831/) (2025)

## Abstract (arXiv)

Single image dehazing, which aims to recover the clear image solely from an input hazy or foggy image, is a challenging ill-posed problem. Analysing existing approaches, the common key step is to estimate the haze density of each pixel. To this end, various approaches often heuristically designed haze-relevant features. Several recent works also automatically learn the features via directly exploiting Convolutional Neural Networks (CNN). However, it may be insufficient to fully capture the intrinsic attributes of hazy images. To obtain effective features for single image dehazing, this paper presents a novel Ranking Convolutional Neural Network (Ranking-CNN). In Ranking-CNN, a novel ranking layer is proposed to extend the structure of CNN so that the statistical and structural attributes of hazy images can be simultaneously captured. By training Ranking-CNN in a well-designed manner, powerful haze-relevant features can be automatically learned from massive hazy image patches. Based on these features, haze can be effectively removed by using a haze density prediction model trained through the random forest regression. Experimental results show that our approach outperforms several previous dehazing approaches on synthetic and real-world benchmark images. Comprehensive analyses are also conducted to interpret the proposed Ranking-CNN from both the theoretical and experimental aspects.