Vision Enhancement in Homogeneous and Heterogeneous Fog

Tarel, Jean-Philippe; Hautiére, Nicolas; Caraffa, Laurent; Cord, A.; Halmaoui, Houssam; Gruyer, Dominique

doi:10.1109/mits.2012.2189969

Cited by 387 publications

(223 citation statements)

References 23 publications

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“…The atmospheric veil can be approximately estimated on the left image using a single image defogging algorithm, see for instance [6,7]. Here, it is approximated by minimizing the following w.r.t.V :…”

Section: Mrf Modelmentioning

confidence: 99%

Stereo Reconstruction and Contrast Restoration in Daytime Fog

Caraffa

Tarel

2013

Lecture Notes in Computer Science

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Abstract. 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 advantage 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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Section: Mrf Modelmentioning

confidence: 99%

Stereo Reconstruction and Contrast Restoration in Daytime Fog

Caraffa

Tarel

2013

Lecture Notes in Computer Science

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“…In order to verify the validity of the proposed method, the defogging results of He [3], Meng [11], Tarel [12], Zhu [5] on natural foggy images are compared. As all of above mentioned de-fog algorithms have strong de-fogging ability on ordinary foggy image, which is difficult to distinguish the advantages and disadvantages of above mentioned algorithms.…”

Section: Experiments On Natural Foggy Imagesmentioning

confidence: 99%

Single Image Defogging Method based on Deep Learning

Yuan¹,

Yang²,

Zhang³

2017

Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)

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Abstract. Single image defogging is a challenging ill-posed problem. Current image defogging methods usually get defogging solutions based on various priors or assumption, which is hardly satisfied in practice. In this paper, a single image defogging method based on deep learning is proposed, in which the priors and assumption do not hold. Firstly, the prediction of transmission map is progressively refined by using three scales convolutional neural networks. Secondly, the fog-free image can be recovered by the atmospheric scattering model after transmission map is got. Experiment results show that the proposed deep learning based defogging algorithm achieves superior performance over state-of-art algorithms on both natural foggy images and synthetic images.

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“…We simulate a dataset composed of 48 foggy images: 12 scenes from [29] are degraded with 4 different realistic fog layers, generated with the software provided in [30]. We compare FVID vs. other recent methods, namely EVID [22], and the methods of [5], [3], and [31].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Fusion-based Variational Image Dehazing

Galdrán

Vázquez-Corral

Pardo

et al. 2016

IEEE Signal Process. Lett.

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Abstract-We propose a novel image dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed Fusion-based Variational Image Dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input. The iterates produced by this minimization are kept, and a second energy that shrinks faster intensity values of well-contrasted regions is minimized, allowing to generate a set of Difference-of-Saturations (DiffSat) maps by observing the shrinking rate. The iterates produced in the first minimization are then fused with these DiffSat maps to produce a haze-free version of the degraded input. The FVID method does not rely on a physical model from which to estimate a depth map, nor it needs a training stage on a database of human-labeled examples. Experimental results on a wide set of hazy images demonstrate that FVID better preserves the image structure on close-by regions that are less affected by fog, and it successfully compares with other current methods in the task of removing haze degradation from far-away regions.

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Vision Enhancement in Homogeneous and Heterogeneous Fog

Cited by 387 publications

References 23 publications

Stereo Reconstruction and Contrast Restoration in Daytime Fog

Stereo Reconstruction and Contrast Restoration in Daytime Fog

Single Image Defogging Method based on Deep Learning

Fusion-based Variational Image Dehazing

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