Journal ArticleDOI
Single Image Haze Removal Using Dark Channel Prior
Kaiming He,Jian Sun,Xiaoou Tang +2 more
TLDR
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.Abstract:Â
In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.read more
Citations
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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.
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.
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.
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
An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data
TL;DR: In this paper, an improved dark-object subtraction technique is demonstrated that allows the user to select a relative atmospheric scattering model to predict the haze values for all the spectral bands from a selected starting band haze value.
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.