Proceedings ArticleDOI
Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
Gaofeng Meng,Ying Wang,Jiangyong Duan,Shiming Xiang,Chunhong Pan +4 more
- pp 617-624
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TLDR
An efficient regularization method to remove hazes from a single input image and can restore a high-quality haze-free image with faithful colors and fine image details is proposed.Abstract:
Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.read more
Citations
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Journal ArticleDOI
DehazeNet: An End-to-End System for Single Image Haze Removal
TL;DR: DehazeNet as discussed by the authors adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing.
Journal ArticleDOI
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
TL;DR: A simple but powerful color attenuation prior for haze removal from a single input hazy image is proposed and outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.
Journal ArticleDOI
LIME: Low-Light Image Enhancement via Illumination Map Estimation
Xiaojie Guo,Yu Li,Haibin Ling +2 more
TL;DR: Experiments on a number of challenging low-light images are present to reveal the efficacy of the proposed LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.
Book ChapterDOI
Single Image Dehazing via Multi-scale Convolutional Neural Networks
TL;DR: A multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps by combining a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale network which refines results locally.
Proceedings ArticleDOI
AOD-Net: All-in-One Dehazing Network
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.
References
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Proceedings ArticleDOI
Fast visibility restoration from a single color or gray level image
TL;DR: A novel algorithm and variants for visibility restoration from a single image which allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera.
Proceedings ArticleDOI
Single image haze removal using dark channel prior
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: A simple but effective image prior - dark channel prior to remove haze from a single input image is proposed, based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel.
Proceedings ArticleDOI
Instant dehazing of images using polarization
TL;DR: This work analyzes the image formation process, taking into account polarization effects of atmospheric scattering, and invert the process to enable the removal of haze from images, and obtains a great improvement of scene contrast and correction of color.
Journal ArticleDOI
Deep photo: model-based photograph enhancement and viewing
Johannes Kopf,Boris Neubert,Billy Chen,Michael Cohen,Daniel Cohen-Or,Oliver Deussen,Matthew T. Uyttendaele,Dani Lischinski +7 more
TL;DR: The results show that augmenting photographs with already available 3D models of the world supports a wide variety of new ways for us to experience and interact with the authors' everyday snapshots.
Proceedings ArticleDOI
Blind Haze Separation
TL;DR: An approach for blindly recovering the parameter needed for separating the airlight from the measurements, thus recovering contrast, with neither user interaction nor existence of the sky in the frame is derived, which eases the interaction and conditions needed for image dehazing.