Proceedings ArticleDOI
Nighttime haze removal based on a new imaging model
Jing Zhang,Yang Cao,Zengfu Wang +2 more
- pp 4557-4561
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TLDR
A novel efficient dehazing method with illumination estimation for nighttime haze condition that can achieve both illumination balanced and haze free results and also has good color rendition ability.Abstract:
Nighttime haze removal is important for different applications such as nighttime video surveillance in haze environment. Different from the imaging conditions in the daytime, nighttime haze images may suffer from non-uniform illumination from artificial light sources. In this paper, we proposed a novel efficient dehazing method with illumination estimation for nighttime haze condition. First, we estimate the light intensity and enhance it to obtain an illumination balanced result. Then, we process a color correction step after estimating the color characteristics of the incident light. Finally, we remove the haze by using the dark channel prior along with estimating the pointwise environmental light. Experimental results show that the proposed method can achieve both illumination balanced and haze free results. Moreover, it also has good color rendition ability.read more
Citations
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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.
Journal ArticleDOI
Semantic Foggy Scene Understanding with Synthetic Data
TL;DR: In this paper, a semi-supervised learning strategy was proposed for semantic foggy scene understanding, which combines supervised learning with an unsupervised supervision transfer from clear-weather images to their synthetic foggy counterparts.
Journal ArticleDOI
Semantic Foggy Scene Understanding with Synthetic Data
TL;DR: In this article, a semi-supervised learning strategy was proposed for semantic foggy scene understanding, which combines supervised learning with an unsupervised supervision transfer from clear-weather images to their synthetic foggy counterparts.
Journal ArticleDOI
Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges
TL;DR: A multi-scale deep neural network for single image dehazing by learning the mapping between hazy images and their 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 dehazed results locally.
Proceedings ArticleDOI
Nighttime Haze Removal with Glow and Multiple Light Colors
TL;DR: A new nighttime haze model is introduced that accounts for the varying light sources and their glow and proposes a framework that first reduces the effect of the glow in the image, resulting in a nighttime image that consists of direct transmission and airlight only.
References
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Proceedings ArticleDOI
Bilateral filtering for gray and color images
Carlo Tomasi,Roberto Manduchi +1 more
TL;DR: In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.
Journal ArticleDOI
Guided Image Filtering
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: The guided filter is a novel explicit image filter derived from a local linear model that can be used as an edge-preserving smoothing operator like the popular bilateral filter, but it has better behaviors near edges.
Journal 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.
Book ChapterDOI
Guided image filtering
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TL;DR: The guided filter is demonstrated that it is both effective and efficient in a great variety of computer vision and computer graphics applications including noise reduction, detail smoothing/enhancement, HDR compression, image matting/feathering, haze removal, and joint upsampling.
Single Image Haze Removal Using Dark Channel Prior
TL;DR: This thesis develops an effective but very simple prior, called the dark channel prior, to remove haze from a single image, and thus solves the ambiguity of the problem.