Baiju P. S.
Skip Abstract Section
Abstract
Nowadays, intelligent transport systems have a major role in providing a safe and secure traffic society for passengers, pedestrians, and vehicles. However, some bad weather conditions such as haze or fog may affect the visual clarity of video footage captured by the camera. This will cause a malfunction in further video processing algorithms performed by such automated systems. This article proposes an efficient technique for estimating the atmospheric light and the transmission map in the haze model entirely in tensor domain for video dehazing. In this work, the atmospheric light is appraised using the Mie scattering principle of visible light and the temporal coherency among the frames is achieved by means of tensor algebra. Furthermore, the transmission map is computed using Low Rank Tensor Approximation (LRTA) based on Weighted Tensor Nuclear Norm (WTNN) minimization and Tensor Total Variation (TTV) regularization. WTNN minimization is used to smooth the coarse transmission map, and TTV regularization is employed to maintain spatio-temporal continuity by preserving the details of salient structures and edges. The novelty of the proposed model is confined in the efficient formulation of a unified optimization model for the estimation of transmission map and atmospheric light in the tensor domain with fine-tuned regularization terms, which is not reported till now in the direction of video dehazing. Extensive experiments show that the proposed method outperforms state-of-the-art methods in video dehazing.
- [1] . 2018. A real-time framework for video Dehazing using bounded transmission and controlled Gaussian filter. Multim. Tools Applic. 77, 20 (2018), 26315–26350. Google Scholar
Digital Library
- [2] . 2018. Twist tensor total variation regularized-reweighted nuclear norm-based tensor completion for video missing area recovery. Inf. Sci. 423 (2018), 376–397. Google ScholarDigital Library
- [3] . 2019. Tensor-based approach for inpainting of video containing sparse text. Multim. Tools Applic. 78, 2 (2019), 1805–1829. Google ScholarDigital Library
- [4] . 2018. Weighted nuclear norm and TV regularization-based image deraining. In 2018 24th National Conference on Communications (NCC). IEEE, 1–6.Google ScholarCross Ref
- [5] . 2010. The comparison of L1 and L2-norm minimization methods. Int. J. Phys. Sci. 5, 11 (2010), 1721–1727.Google Scholar
- [6] . 2012. An explicit sharpness index related to global phase coherence. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1065–1068.Google ScholarCross Ref
- [7] . 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1 (2011), 1–122. Google ScholarDigital Library
- [8] . 2016. Real-time video dehazing based on spatio-temporal MRF. In Pacific Rim Conference on Multimedia. Springer, 315–325.Google ScholarCross Ref
- [9] . 2016. Total variation regularized tensor RPCA for background subtraction from compressive measurements. IEEE Trans. Image Process. 25, 9 (2016), 4075–4090.Google ScholarDigital Library
- [10] . 2016. Total variation regularized RPCA for irregularly moving object detection under dynamic background. IEEE Trans. Cyber. 46, 4 (2016), 1014–1027.Google ScholarCross Ref
- [11] . 1970. Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika 35, 3 (1970), 283–319.Google ScholarCross Ref
- [12] . 2015. An advanced visibility restoration algorithm for single hazy images. ACM Trans. Multim. Comput., Commun. Applic. 11, 4 (2015), 1–21. Google ScholarDigital Library
- [13] . 2016. Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In European Conference on Computer Vision. Springer, 576–591.Google ScholarCross Ref
- [14] . 2014. Tensors: A brief introduction. IEEE Sig. Process. Mag. 31, 3 (
May 2014), 44–53.DOI: https://doi.org/10.1109/MSP.2014.2298533Google ScholarCross Ref - [15] . 2020. \(D^{2}ehazing\): Real-time dehazing in traffic video analytics by fast dynamic bilateral filtering. In 3rd International Conference on Computer Vision and Image Processing. Springer, 127–137.Google ScholarCross Ref
- [16] . 2000. A multilinear singular value decomposition. SIAM J. Matrix Anal. Applic. 21, 4 (2000), 1253–1278. Google ScholarDigital Library
- [17] . 2019. Efficient traffic video dehazing using adaptive dark channel prior and spatial–temporal correlations. Sensors 19, 7 (2019), 1593.Google ScholarCross Ref
- [18] . 2010. Contrast enhancement of images using partitioned iterated function systems. Image Vis. Comput. 28, 1 (2010), 45–54. Google ScholarDigital Library
- [19] . 2018. Underwater image and video dehazing with pure haze region segmentation. Comput. Vis. Image Underst. 168 (2018), 145–156. Google ScholarDigital Library
- [20] . 2012. An investigation of dehazing effects on image and video coding. IEEE Trans. Image Process. 21, 2 (
Feb. 2012), 662–673.DOI: https://doi.org/10.1109/TIP.2011.2166968 Google ScholarDigital Library - [21] . 2008. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. 27, 2 (2008), 87–95.Google ScholarCross Ref
- [22] . 2011. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 12 (
Dec. 2011), 2341–2353.DOI: https://doi.org/10.1109/TPAMI.2010.168 Google ScholarDigital Library - [23] . 2012. Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 6 (2012), 1397–1409. Google ScholarDigital Library
- [24] . 2018. Anisotropic total variation regularized low-rank tensor completion based on tensor nuclear norm for color image inpainting. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1363–1367.Google ScholarCross Ref
- [25] . 2015. Tensor–tensor products with invertible linear transforms. Linear Algeb. Appl. 485 (2015), 545–570.Google ScholarCross Ref
- [26] . 2013. Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM J. Matrix Anal. Appl. 34, 1 (2013), 148–172.Google ScholarDigital Library
- [27] . 2013. Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Repres. 24, 3 (2013), 410–425. Google ScholarDigital Library
- [28] . 2018. End-to-end united video dehazing and detection. In 32nd AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- [29] . 2018. Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28, 1 (2018), 492–505. Google ScholarDigital Library
- [30] . 2019. Image/Video restoration via multiplanar autoregressive model and low-rank optimization. ACM Trans. Multim. Comput., Commun. Applic. 15, 4 (2019), 1–23. Google ScholarDigital Library
- [31] . 2015. Simultaneous video defogging and stereo reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4988–4997.
DOI: https://doi.org/10.1109/CVPR.2015.7299133Google ScholarCross Ref - [32] . 2018. Robust electric network frequency estimation with rank reduction and linear prediction. ACM Trans. Multim. Comput., Commun. Applic. 14, 4 (2018), 1–13. Google ScholarDigital Library
- [33] . 2018. Fast video dehazing using per-pixel minimum adjustment. Math. Prob. Eng. 2018 (2018), 1–8.
DOI: https://doi.org/10.1155/2018/9241629Google ScholarCross Ref - [34] . 2018. DCT-based weighted adaptive multi-linear data completion and denoising. Neurocomputing 318 (2018), 120–136.Google ScholarCross Ref
- [35] . 2013. An order-p tensor factorization with applications in imaging. SIAM J. Sci. Comput. 35, 1 (2013), A474–A490.Google ScholarCross Ref
- [36] . 1976. Optics of the Atmosphere: Scattering by Molecules and Particles. New York, John Wiley and Sons, Inc.Google Scholar
- [37] . 2012. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 12 (2012), 4695–4708. Google ScholarDigital Library
- [38] . 2012. Making a “completely blind” image quality analyzer. IEEE Sig. Process. Lett. 20, 3 (2012), 209–212.Google ScholarCross Ref
- [39] . 2018. Fast execution schemes for dark-channel-prior-based outdoor video dehazing. IEEE Access 6 (2018), 10003–10014.
DOI: https://doi.org/10.1109/ACCESS.2018.2806378Google ScholarCross Ref - [40] . 2018. Gated fusion network for single image dehazing. In IEEE Conference on Computer Vision and Pattern Recognition. 3253–3261.Google ScholarCross Ref
- [41] . 2020. Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int. J. Comput. Vis. 128, 1 (2020), 240–259.Google ScholarDigital Library
- [42] . 2019. Deep video dehazing with semantic segmentation. IEEE Trans. Image Process. 28, 4 (2019), 1895–1908.Google ScholarDigital Library
- [43] . 2014. Video dehazing without flicker artifacts using adaptive temporal average. In 18th IEEE International Symposium on Consumer Electronics (ISCE). 1–2.
DOI: https://doi.org/10.1109/ISCE.2014.6884454Google Scholar - [44] . 2017. Parallel implementation and optimization of high definition video real-time dehazing. Multim. Tools Applic. 76, 22 (2017), 23413–23434. Google ScholarDigital Library
- [45] . 2018. Tensor total variation regularized moving object detection for surveillance videos. In International Conference on Signal Processing and Communications (SPCOM). IEEE, 327–331.Google ScholarCross Ref
- [46] . 2020. Simultaneous reconstruction and moving object detection from compressive sampled surveillance videos. IEEE Trans. Image Process. 29 (2020), 7590–7602.Google ScholarDigital Library
- [47] . 2021. A three-way optimization technique for noise robust moving object detection using tensor low-rank approximation, l1/2, and TTV regularizations. IEEE Trans. Cyber. 51, 2 (2021), 1004–1014.
DOI: https://doi.org/10.1109/TCYB.2019.2921827Google ScholarCross Ref - [48] . 2019. Dark channel prior-based video dehazing algorithm with sky preservation and its embedded system realization for ADAS applications. Optics Expr. 27, 9 (2019), 11877–11901.Google ScholarCross Ref
- [49] . 2018. A component-driven distributed framework for real-time video dehazing. Multim. Tools Applic. 77, 9 (2018), 11259–11276. Google ScholarDigital Library
- [50] . 2012. Fast image dehazing using guided joint bilateral filter. Vis. Comput. 28, 6–8 (2012), 713–721. Google ScholarDigital Library
- [51] . 2013. An efficient ADMM algorithm for multidimensional anisotropic total variation regularization problems. In 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 641–649. Google ScholarDigital Library
- [52] . 2005. Generalized low rank approximations of matrices. Mach. Learn. 61, 1–3 (2005), 167–191. Google ScholarDigital Library
- [53] . 2011. Image defogging: A survey. J. Image Graph. 16, 9 (2011), 1561–1576.Google Scholar
- [54] . 2011. Video dehazing with spatial and temporal coherence. Vis. Comput. 27, 6–8 (2011), 749–757. Google ScholarDigital Library
- [55] . 2014. Novel methods for multilinear data completion and de-noising based on tensor-SVD. In IEEE Conference on Computer Vision and Pattern Recognition. 3842–3849. Google ScholarDigital Library
- [56] . 2018. Haze removal for unmanned aerial vehicle aerial video based on spatial-temporal coherence optimisation. IET Image Process. 12, 1 (2018), 88–97.Google ScholarCross Ref
- [57] . 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13, 4 (
Apr. 2004), 600–612.DOI: https://doi.org/10.1109/TIP.2003.819861 Google ScholarDigital Library - [58] . 2016. Non-local sparse and low-rank regularization for structure-preserving image smoothing. In Computer Graphics Forum, Vol. 35. Wiley Online Library, 217–226. Google ScholarDigital Library
- [59] . 2015. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24, 11 (2015), 3522–3533.Google ScholarDigital Library
Index Terms
- TTV Regularized LRTA Technique for the Estimation of Haze Model Parameters in Video Dehazing
Recommendations
Twist tensor total variation regularized-reweighted nuclear norm based tensor completion for video missing area recovery
This paper focuses on recovering multi-dimensional signal called tensor which is corrupted by random missing areas. The performance of the conventional tensor completion techniques deteriorate when the tensor multi-rank is large and/or large missing ...
A convex single image dehazing model via sparse dark channel prior
AbstractIn this paper, we present a convex model for single image dehazing via a sparse dark channel prior. Our work is based on an observation that the number of bright pixels is very small in the dark channel of a haze-free image, but ...
Loose L1/2 regularised sparse representation for face recognition
Sparse representation (or sparse coding) has been applied to deal with frontal face recognition. Two representative methods are the sparse representation‐based classification (SRC) and the collaborative representation‐based classification (CRC), in which ...
Comments