AOD-Net: All-In-One Dehazing Network
Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng
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.
BibTeX
@inproceedings{iccv2017_aodnetallinonede,
title = {AOD-Net: All-In-One Dehazing Network},
author = {Boyi Li and Xiulian Peng and Zhangyang Wang and Jizheng Xu and Dan Feng},
booktitle = {ICCV 2017},
year = {2017}
}