Markov Random Field model for single image defogging

Caraffa, Laurent; Tarel, Jean-Philippe

doi:10.1109/ivs.2013.6629596

Cited by 45 publications

(38 citation statements)

References 12 publications

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“…Most recent algorithms improve [8] only in some particular aspects. For example, Tarel and Hautiere [19] replace matting with "median of median along lines" filter for efficiency, Gibson et al [7] with standard median filter, Yu et al [21] with joint bilateral filter; Tarel et al [20] adds planar constraint for road images; Caraffa et al [4] adds planar constraint and noise model.…”

Section: Introductionmentioning

confidence: 99%

Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing

Tang

Yang

Wang

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

529

332

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Haze is one of the major factors that degrade outdoor images. Removing haze from a single image is known to be severely ill-posed, and assumptions made in previous methods do not hold in many situations. In this paper, we systematically investigate different haze-relevant features in a learning framework to identify the best feature combination for image dehazing. We show that the dark-channel feature is the most informative one for this task, which confirms the observation of He et al.[8] from a learning perspective, while other haze-relevant features also contribute significantly in a complementary way. We also find that surprisingly, the synthetic hazy image patches we use for feature investigation serve well as training data for realworld images, which allows us to train specific models for specific applications. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world datasets.

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Section: Introductionmentioning

confidence: 99%

Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing

Tang

Yang

Wang

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

529

332

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“…By assuming the transmission and surface shading are locally uncorrelated, Fattal (R. Fattal, 2008) estimates the haze by independent component analysis and then infers the medium transmission and the color, but can't work well for heavy fog image. Some novel dehazing methods from a single image using a Markov random field (MRF) framework are also described in recent papers (Fan Guo, 2014a andTarel 2013). Tarel and Hautiere proposed a median filter-based single image visibility restoration algorithm which can effectively preserve both edges and corners (JP Tarel and N.Hautiere, 2009); In addition, Ketan Tang et al (Ketan Tang, Jianchao Yang, Jue Wang, 2014) try a learning-based new idea for single image dehazing by using random forest to learn a regression model for transmission estimation of hazy images.…”

Section: Introductionmentioning

confidence: 99%

Single Image Dehazing for Visibility Improvement

Zhai

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In this paper, we propose a new transmission estimated method to improve the visibility of single input image (with fog or haze), as well as the image's details. Our approach stems from two important statistical observations about haze-free images and the haze itself. First, the famous dark channel prior, a statistics of the haze-free outdoor images, can be used to estimate the thickness of the haze; and second, gradient prior law of transmission maps, which is based on dark channel prior. By integrating these two priors, to estimate the unknown scene transmission map is modeled into a TV-regularization optimization problem. The experimental results show that the proposed approach can effectively improve the visibility and keep the details of fog degraded images in the meanwhile.

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“…0L We propose to rely on an approximate atmospheric veilV to find the approximatesD andÏ 0L . The atmospheric veil can be approximately estimated on the left image using a single image defogging algorithm, see for instance [2], [13], [15]. Here, it is approximated by minimizing the following w.r.t.V:…”

Section: Prior Termmentioning

confidence: 99%

“…Figure 5 shows an example of defogging obtained using a single image defogging algorithm described in Ref. [2].…”

Section: Refinement Using Second Order Priormentioning

confidence: 99%

See 1 more Smart Citation

Combining Stereo and Atmospheric Veil Depth Cues for 3D Reconstruction

Caraffa¹,

Tarel²

2014

IPSJ Transactions on Computer Vision and Applications

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Stereo reconstruction serves many outdoor applications, and thus sometimes faces foggy weather. The quality of the reconstruction by state of the art algorithms is then degraded as contrast is reduced with the distance because of scattering. However, as shown by defogging algorithms from a single image, fog provides an extra depth cue in the gray level of far away objects. Our idea is thus to take advantages of both stereo and atmospheric veil depth cues to achieve better stereo reconstructions in foggy weather. To our knowledge, this subject has never been investigated earlier by the computer vision community. We thus propose a Markov Random Field model of the stereo reconstruction and defogging problem which can be optimized iteratively using the α-expansion algorithm. Outputs are a dense disparity map and an image where contrast is restored. The proposed model is evaluated on synthetic images. This evaluation shows that the proposed method achieves very good results on both stereo reconstruction and defogging compared to standard stereo reconstruction and single image defogging.

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Markov Random Field model for single image defogging

Cited by 45 publications

References 12 publications

Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing

Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing

Single Image Dehazing for Visibility Improvement

Combining Stereo and Atmospheric Veil Depth Cues for 3D Reconstruction

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