# Improved Techniques for Learning to Dehaze and Beyond: A Collective Study

arXiv: https://arxiv.org/abs/1807.00202 · HTML: https://mlsanity.com/paper/1807.00202/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network](https://mlsanity.com/paper/2106.14501/) (2021)
- [Diffusion Models in Low-Level Vision: A Survey](https://mlsanity.com/paper/2406.11138/) (2024)
- [Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing](https://mlsanity.com/paper/2308.06998/) (2023)
- [Debiased Subjective Assessment of Real-World Image Enhancement](https://mlsanity.com/paper/2106.10080/) (2021)
- [Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze](https://mlsanity.com/paper/2103.07065/) (2021)
- [WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing](https://mlsanity.com/paper/2505.04369/) (2025)
- [A GAN-Based Input-Size Flexibility Model for Single Image Dehazing](https://mlsanity.com/paper/2102.09796/) (2021)

## Abstract (arXiv)

Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e.g., object detection) of hazy images. For the first task, we investigated a variety of loss functions and show that perception-driven loss significantly improves dehazing performance. In the second task, we provide multiple solutions including using advanced modules in the dehazing-detection cascade and domain-adaptive object detectors. In both tasks, our proposed solutions significantly improve performance. GitHub repository URL is: https://github.com/guanlongzhao/dehaze