Proceedings Article10.1109/ICCV.2017.511
AOD-Net: All-in-One Dehazing Network
Boyi Li,Xiulian Peng,Zhangyang Wang,Xu Jizheng,Dan Feng +4 more
- 01 Oct 2017
- pp 4780-4788
2K
TL;DR: An image dehazing model built with a convolutional neural network (CNN) based on a re-formulated atmospheric scattering model, called All-in-One Dehazing Network (AOD-Net), which demonstrates superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality.
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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.
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Citations
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Thermal Infrared Single Image Dehazing and Blind Image Quality Assessment
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Dehazing cost volume for deep multi-view stereo in scattering media with airlight and scattering coefficient estimation
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6
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