# A comprehensive benchmark analysis for sand dust image reconstruction

arXiv: https://arxiv.org/abs/2202.03031 · HTML: https://mlsanity.com/paper/2202.03031/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields](https://mlsanity.com/paper/1502.07411/) (2015)
- [Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning](https://mlsanity.com/paper/1904.05050/) (2019)
- [O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images](https://mlsanity.com/paper/1804.05101/) (2018)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing](https://mlsanity.com/paper/2001.06968/) (2020)
- [Syn2Real Transfer Learning for Image Deraining using Gaussian Processes](https://mlsanity.com/paper/2006.05580/) (2020)
- [DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal](https://mlsanity.com/paper/2008.00767/) (2020)
- [Nighttime Dehazing with a Synthetic Benchmark](https://mlsanity.com/paper/2008.03864/) (2020)
- [Robust Representation Learning with Feedback for Single Image Deraining](https://mlsanity.com/paper/2101.12463/) (2021)
- [All-In-One Underwater Image Enhancement using Domain-Adversarial Learning](https://mlsanity.com/paper/1905.13342/) (2019)
- [Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network](https://mlsanity.com/paper/2008.02763/) (2020)
- [Deep Underwater Image Enhancement](https://mlsanity.com/paper/1807.03528/) (2018)
- [UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing](https://mlsanity.com/paper/1912.10269/) (2019)
- [DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks](https://mlsanity.com/paper/1908.10267/) (2019)
- [Dehaze-GLCGAN: Unpaired Single Image De-hazing via Adversarial Training](https://mlsanity.com/paper/2008.06632/) (2020)

## Built on by (most cited first, 3 of 3)

- [SANDFORMER: CNN and Transformer under Gated Fusion for Sand Dust Image Restoration](https://mlsanity.com/paper/2303.04365/) (2023)
- [Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function](https://mlsanity.com/paper/2307.15230/) (2023)
- [AOSR-Net: All-in-One Sandstorm Removal Network](https://mlsanity.com/paper/2309.08838/) (2023)

## Abstract (arXiv)

Numerous sand dust image enhancement algorithms have been proposed in recent years. To our best acknowledge, however, most methods evaluated their performance with no-reference way using few selected real-world images from internet. It is unclear how to quantitatively analysis the performance of the algorithms in a supervised way and how we could gauge the progress in the field. Moreover, due to the absence of large-scale benchmark datasets, there are no well-known reports of data-driven based method for sand dust image enhancement up till now. To advance the development of deep learning-based algorithms for sand dust image reconstruction, while enabling supervised objective evaluation of algorithm performance. In this paper, we presented a comprehensive perceptual study and analysis of real-world sand dust images, then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for training Convolutional Neural Networks (CNNs) and evaluating algorithms performance. In addition, we adopted the existing image transformation neural network trained on SIRB as baseline to illustrate the generalization of SIRB for training CNNs. Finally, we conducted the qualitative and quantitative evaluation to demonstrate the performance and limitations of the state-of-the-arts (SOTA), which shed light on future research in sand dust image reconstruction.