@inproceedings{li2017aod-net,
author = {Li, Boyi and Peng, Xiulian and Wang, Zhangyang and Xu, Ji-Zheng and Feng, Dan},
title = {AOD-Net: all-in-one dehazing network},
booktitle = {International Conference on Computer Vision},
year = {2017},
month = {October},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/aod-net-all-in-one-dehazing-network/},
}