AOD-Net: All-in-One Dehazing Network


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.


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]



Note: If you could not download successfully, you could also find this code in github .


[training images] [original images]

Note: If you use this synthetic dataset, you should also cite NYU2 paper[bibtex].



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}



Here shows our work on Improving High-level Tasks with Dehazing. Details can be found in our paper.