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
Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut
TL;DR: This paper reduces the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method, which yields better images than those from the existing state-of-the-art-methods.
Journal ArticleDOI
A Single Image Dehazing Technique Using the Dual Transmission Maps Strategy and Gradient-Domain Guided Image Filtering
TL;DR: In this article, a single image dehazing technique using dual transmission maps strategy and gradient-domain guided image filtering is presented, which not only removes halo artifacts and reduces the saturation but also ensures the natural appearance in the recovered images.
Posted Content
Benchmarking Single Image Dehazing and Beyond
TL;DR: The Realistic Single Image DEhazing (RESIDE) benchmark as discussed by the authors is a large-scale benchmark consisting of both synthetic and real-world hazy images, which highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes.
Journal ArticleDOI
A feature-supervised generative adversarial network for environmental monitoring during hazy days
TL;DR: Results show that the proposed feature-supervised learning network based on generative adversarial networks for environmental monitoring during hazy days has achieved better performance than current state-of-the-art methods on both synthetic datasets and real-world remote sensing images.
Posted Content
Learning Dual Convolutional Neural Networks for Low-Level Vision
Jinshan Pan,Sifei Liu,Deqing Sun,Jiawei Zhang,Yang Liu,Jimmy Ren,Zechao Li,Jinhui Tang,Huchuan Lu,Yu-Wing Tai,Ming-Hsuan Yang +10 more
TL;DR: DualCNN as mentioned in this paper consists of two parallel branches, which respectively recover the structures and details in an end-to-end manner, which can generate the target signals according to the formation model for each particular application.
References
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Proceedings ArticleDOI
Visibility in bad weather from a single image
TL;DR: A cost function in the framework of Markov random fields is developed, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation, and is applicable for both color and gray images.
Journal ArticleDOI
Single image dehazing
TL;DR: Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.
Journal ArticleDOI
Contrast restoration of weather degraded images
TL;DR: A physics-based model is presented that describes the appearances of scenes in uniform bad weather conditions and a fast algorithm to restore scene contrast, which is effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols.
Journal ArticleDOI
Edge-preserving decompositions for multi-scale tone and detail manipulation
TL;DR: This paper advocates the use of an alternative edge-preserving smoothing operator, based on the weighted least squares optimization framework, which is particularly well suited for progressive coarsening of images and for multi-scale detail extraction.
Journal ArticleDOI
Vision and the Atmosphere
TL;DR: This work studies the visual manifestations of different weather conditions, and model the chromatic effects of the atmospheric scattering and verify it for fog and haze, and derives several geometric constraints on scene color changes caused by varying atmospheric conditions.