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A real-time framework for video Dehazing using bounded transmission and controlled Gaussian filter

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Abstract

The haze phenomenon exerts a degrading effect that decreases contrast and causes color shifts in outdoor images and videos. The presence of haze in outdoor images and videos is bothersome, unpleasant, and occasionally, even dangerous. Atmospheric light scattering (ALS) model is widely used to restore hazy images. In this model, two unknown parameters should be estimated: airlight and scene transmission. The quality of dehazed images and video frames considerably depends on those two parameters as well as on the speed and accuracy of the refinement process of the approximated scene transmission, this refinement is necessary to ensure spatial coherency of the output dehazed video. Spatial coherency should be accounted for in order to eliminate flickering artifacts usually noticed when extending single-image dehazing methods to the video scenario. Classic methods typically require high computation capacity in order to dehaze videos in real time. However, when the driver assistance context is considered, these approaches are inappropriate due to the limited resources mobile environments usually have. To address this issue, this study proposes a framework for real-time video dehazing. This framework consists of two stages: single-image dehazing using the bounded transmission (BT) method, which is utilized to dehaze single video frame in real time with high accuracy; and transmission refinement stage using a filter we call controlled Gaussian filter (CGF), which is proposed for the linear and simplified refinement of the scene transmission. To evaluate the proposed framework, three image datasets in addition to two video streams are employed. Experimental results show that the single-image stage in the proposed framework is at least seven times faster than existing methods. In addition, the analysis of variance (ANOVA) test proves that the quality of dehazed images in this stage is statistically similar to or better than those obtained using existing methods. Also, experiments show that the video stage in the proposed framework is capable of real-time video dehazing with better quality than the existing methods.

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Abbreviations

BT:

Bounded Transmission

Dehazing:

Scene restoration from haze and similar phenomena effects

ALS:

Airlight scattering

CGF:

Controlled Gaussian filter

AL:

Atmospheric light, airlight or ambient light

RGB:

Red, green and blue color space

DCP:

Dark channel prior

fps:

Frames per second

IV-M:

Images and videos captured in Malaysia

FRIDA:

Foggy road image database

BRISQUE:

Blind referenceless image spatial quality evaluator

NR IQA:

No-reference image quality assessment

FR IQA:

Full-reference image quality assessment

RR IQA:

Reduced-reference image quality assessment

MAD:

Mean absolute difference

ALII:

Airlight by image integral

MANOVA:

Multivariate analysis of variance

ANOVA:

Analysis of variance

PA:

Planar assumption

MOS:

Mean opinion score

PLCC:

Pearson linear correlation coefficient

SRCC:

Spearman’s rank-order correlation coefficient

WMF:

Weighted median filter

DoG:

Difference of Gaussians

MSE:

Mean square error

OpenCV:

Open computer vision library

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Author information

Authors and Affiliations

  1. Faculty of Science and Technology, Universiti Sains Islam, Malaysia (USIM), 71800, Negeri Sembilan, Malaysia

    Ahmad Alajarmeh

  2. Department of Computing Faculty of Arts, Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjong Malim, Perak, Malaysia

    A. A. Zaidan

Authors
  1. Ahmad Alajarmeh
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  2. A. A. Zaidan
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Corresponding author

Correspondence to A. A. Zaidan.

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The authors declare that they have no competing interests.

Appendices

Appendix 1

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Global Dataset

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Appendix 2

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Subset of FRIDA Dataset

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Appendix 3

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Subset of IV-M Dataset

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Appendix 4

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Selected frames from “UPMhaze” video stream

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Appendix 5

Fig. 29
figure 29

Selected frames from “River Side” video stream

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Alajarmeh, A., Zaidan, A.A. A real-time framework for video Dehazing using bounded transmission and controlled Gaussian filter. Multimed Tools Appl 77, 26315–26350 (2018). https://doi.org/10.1007/s11042-018-5861-4

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  • DOI: https://doi.org/10.1007/s11042-018-5861-4

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