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
Binocular Image Dehazing via a Plain Network without Disparity Estimation
TL;DR: Zhang et al. as discussed by the authors proposed a plain binocular image dehazing network, called BidNet, to dehaze both the left and right images simultaneously by mining the relationship and correlation between binocular images, making the best of varying information of cross views.
7
Learning of Image Dehazing Models for Segmentation Tasks
Sebastien de Blois,Ihsen Hedhli,Christian Gagné +2 more
- 04 Mar 2019
TL;DR: This work proposes a novel end-to-end approach for image dehazing, fit for being used as input to an image segmentation procedure, while maintaining the visual quality of the generated images.
7
Compound Multi-branch Feature Fusion for Real Image Restoration
TL;DR: This paper proposes a multi-branch restoration model inspired from the Human Visual System (i.e., Retinal Ganglion Cells) which can achieve multiple restoration tasks in a general framework and has competitive performance results on four datasets.
7
Robust Haze and Thin Cloud Removal via Conditional Variational Autoencoders
Haidong Ding,Fengying Xie,Linwei Qiu,Xiaozhe Zhang,Zhen Xia Shi +4 more
TL;DR: A novel algorithm for haze and thin cloud removal using conditional variational autoencoders (CVAEs) to generate multiple realistic restored images for each input and a dynamic fusion network (DFN) for combining multiple plausible outcomes to obtain a more accurate result.
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Bacterial Foraging-Fuzzy synergism based Image Dehazing
TL;DR: Comparative qualitative, quantitative and run-time complexity analyses’ results proved the excellence of the proposed work over several state-of-the art methods.
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