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
Single Image Dehazing Via Artificial Multiple Shots And Multidimensional Context
Wei Haoran,Qingbo Wu,Hui Li,King Ngi Ngan,Hongliang Li,Fanman Meng +5 more
- 01 Oct 2020
TL;DR: This paper proposes to generate artificial multiple shots for simulating the images captured under different haze degrees, and two context reasoning modules are developed to describe the relationship across different spatial regions and artificial shots.
11
AGLC-GAN: Attention-based global-local cycle-consistent generative adversarial networks for unpaired single image dehazing
R. S. Jaisurya,Snehasis Mukherjee +1 more
TL;DR: This study proposes AGLC-GAN, an attention-based generative adversarial network for unpaired single image dehazing, achieving significant quantitative and qualitative improvements over existing methods by incorporating channel and pixel attention, cyclic perceptual consistency loss, and dynamic feature enhancement.
11
Single Image Dehazing Using End-to-End Deep-Dehaze Network
TL;DR: This study offers the Deep-Dehaze network to retrieve haze-free images by training the image translation and dehazing network in an end-to-end manner and validated it on natural and synthetic hazy datasets.
11
•Posted Content
Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing
TL;DR: A new fully non-homogeneous atmospheric scattering model (FNH-ASM) is proposed for well modeling the hazy images under complex conditions where ALF and ASC are pixel dependent, and an end-to-end CNN-based dehazing network, FNHD-Net, is developed, which applies beta-Loss and D-L Loss.
11
A GAN-based input-size flexibility model for single image dehazing
TL;DR: Based on the atmospheric scattering model, a novel model is designed to directly generate the haze-free image in this article , where a simple and effective U-connection residual network (UR-Net) is proposed to combine the generator and adopt the spatial pyramid pooling (SPP) to design the discriminator.
References
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
•Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
25.3K
•Proceedings Article
Faster R-CNN: towards real-time object detection with region proposal networks
Shaoqing Ren,Kaiming He,Ross Girshick,Jian Sun +3 more
- 07 Dec 2015
TL;DR: Ren et al. as discussed by the authors proposed a region proposal network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.
The Pascal Visual Object Classes Challenge: A Retrospective
TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.