Proceedings Article10.1109/ICCV.2017.511
AOD-Net: All-in-One Dehazing Network
Boyi Li,Xiulian Peng,Zhangyang Wang,Xu Jizheng,Dan Feng +4 more
- 01 Oct 2017
- pp 4780-4788
2K
TL;DR: An image dehazing model built with a convolutional neural network (CNN) based on a re-formulated atmospheric scattering model, called All-in-One Dehazing Network (AOD-Net), which demonstrates superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality.
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Abstract: This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level tasks on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN, we witness a large improvement of the object detection performance on hazy images.
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Citations
Object Detection in Hazy Environment Enhanced by Preprocessing Image Dataset with Synthetic Haze
Binghan Li,Yindong Hua,Mi Lu +2 more
- 01 Dec 2020
TL;DR: Zhang et al. as discussed by the authors presented a simple and flexible algorithm to generate synthetic haze to MS COCO training dataset, which aims to enhance the performance of object detection in haze when taking the new synthesized hazy images as training dataset.
6
•Posted Content
Unsupervised Neural Rendering for Image Hazing.
TL;DR: Zhang et al. as mentioned in this paper proposed a knowledge-driven neural network which estimates the transmission map by leveraging a new prior, i.e., there exists the structure similarity (e.g., contour and luminance) between the transmission maps and the input clean images.
6
PhDnet: A novel physic-aware dehazing network for remote sensing images
Ziyang Lihe,Jianguo He,Qiangqiang Yuan,Xianyu Jin,Yi Xiao,Liangpei Zhang +5 more
TL;DR: This study introduces PhDnet, a novel physic-aware dehazing network for remote sensing images, combining residual learning with atmospheric scattering models, achieving effective haze removal with physical interpretability and outperforming prior methods on synthetic and real-world datasets.
6
•Posted Content
PAD-Net: A Perception-Aided Single Image Dehazing Network
Yu Liu,Guanlong Zhao +1 more
TL;DR: Objective experimental results suggest that by merely changing the loss function, the authors can obtain significantly higher PSNR and SSIM scores on the SOTS set in the RESIDE dataset, compared with a state-of-the-art end-to-end dehazing neural network (AOD-Net) that uses the $\ell_2$ loss.
Streamlined Global and Local Features Combinator (SGLC) for High Resolution Image Dehazing
Bilel Benjdira,Anas M. Ali,Anis Koubâa +2 more
- 01 Jun 2023
TL;DR: SGLC effectively combines global and local features to enhance the performance of high-resolution image dehazing models.
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