AOD-Net: All-in-One Dehazing Network
Proceedings of 18th IEEE International Conference on Computer Vision (ICCV), 2017
Abstract
We 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.
Paper
B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, “AOD-Net: All-in-One Dehazing Network”, In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. [PDF]
Download
[Code]
Note: If you could not download successfully, you could also find this code in github .
dataset
[training images] [original images]
Note: If you use this synthetic dataset, you should also cite NYU2 paper[bibtex].
Bibtex
@InProceedings{Li_2017_ICCV,
author = {Li, Boyi and Peng, Xiulian and Wang, Zhangyang and Xu, Jizheng and Feng, Dan},
title = {AOD-Net: All-In-One Dehazing Network},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
Video
Here shows our work on Improving High-level Tasks with Dehazing. Details can be found in our paper.