Aod-net: All-in-one dehazing network
Proceedings of the IEEE international conference on computer …, 2017•openaccess.thecvf.com
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, eg, Faster R-CNN, for improving high-level tasks …
(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, eg, Faster R-CNN, for improving high-level tasks …
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, eg, 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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