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
DCNet: Dark Channel Network for single-image dehazing
Akshay Bhola,Teena Sharma,Nishchal K. Verma +2 more
- 01 May 2021
TL;DR: Wang et al. as mentioned in this paper proposed a dark channel network to estimate the transmission map of an input hazy scene for single-image dehazing, which outperformed the existing models in terms of standard quantitative metrics such as mean square error, structural similarity index, and peak signal to noise ratio.
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Oleksii Sidorov,Congcong Wang,Faouzi Alaya Cheikh +2 more
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TL;DR: This paper proposes a novel approach for computational smoke removal using supervised image-to-image translation of GAN's architecture and adding perceptual image quality metric to the loss function, and demonstrates that proposed method efficiently removes smoke as well as preserves perceptually sufficient image quality.
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Aethra-net: Single image and video dehazing using autoencoder
Akshay Juneja,Sunil Kumar Singla +1 more
TL;DR: In this paper , a gush enhancer-based autoencoder is modified to obtain the transmission map, which resembles the processing of light entering the human eye from different paths.
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CGGAN: a context-guided generative adversarial network for single image dehazing
Zhaorun Zhou,Zhenghao Shi +1 more
TL;DR: Zhang et al. as mentioned in this paper proposed a novel context-guided generative adversarial network (CGGAN) for single image dehazing, which consists of a feature-extraction net, a context extraction net, and a fusion net in sequence.
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Single Remote Sensing Multispectral Image Dehazing Based on a Learning Framework
TL;DR: A novel and effective algorithm based on a learning framework to improve the visibility of a single hazy remote sensing multispectral image and a more effective method to estimate the atmospheric light, which can restrain the influence of highlight areas on the atmosphericLight acquisition.
10
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