f An Optimized Stationary Wavelet Fusion Technique for Image de-hazing
FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-120408

Research Article |

An Optimized Stationary Wavelet Fusion Technique for Image de-hazing

Author(s): Tapasmini Sahoo* and Kunal Kumar Das

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 4

Publisher : FOREX Publication

Published : 25 October 2024

e-ISSN : 2347-470X

Page(s) : 1181-1187

DOI: https://doi.org/10.37391/IJEER.120408

Abstract

Nowadays, the amount of smoke and dust in the air is increasing significantly due to industrialization. The smoke and dust particles accumulate in the relatively dry air and cause haze in the surrounding area, impairs visibility. This haze also affects photography, which reduces the images' quality and looks unnatural. The hazy atmosphere affects even pictures taken with a cell phone in everyday life. There are many methods to remove this haze content from the image, but they have not yielded great results. The long-time and short-time shots constantly differed while attempting to eliminate atmospheric haze from the images. To solve this problem, a fusion rule was proposed to fuse the luminance and dark channel prior (DCP) methods. The transmission estimated with the DCP method contributes mainly to the foreground regions, while the luminance model deals with the celestial regions. The fusion technique is a pixel-level fusion approach in the transform domain. The proposed approach combines the transmittance values obtained from the dark channel in front of the foreground region (background) and the luminance model for the sky region in the transform domain using the Stationary Wavelet Transform (SWT) with the optimized level of decomposition. The proposed algorithm was subjected to quantitative analysis of some statistical measures. The result shows that the proposed method successfully maintains the maximum visual truth content by effectively removing atmospheric haze from the images.

Keywords: Haze, Fusion Rule, Dark Channel Prior (DCP), luminance, Stationary Wavelet Transform.




Tapasmini Sahoo*, Institute of Technical Education and Research, SOA (Deemed to be University); Email: tapasminisahoo@soa.ac.in

Kunal Kumar Das, Institute of Technical Education and Research, SOA (Deemed to be University); Email: kunaldas@soa.ac.in

    [1] K. He, J. Sun, X. Tang, Single image haze removal using dark channel prior, IEEE Trans. Pattern Anal. Mach. Intell. 33 (12) (2011) 2341–2353.
    [2] G. Wang, G. Ren, L. Jiang, T. Quan, Single image dehazing algorithm based on sky region segmentation, Inf. Technol. J. 12 (6) (2013) 1168–1175.
    [3] Z. Shi , J. Long , W. Tang , C. Zhang ,Single image dehazing in inhomogeneous atmosphere, Optik - Int. J. Light Electron Opt. 125 (15) (2014) 3868–3875 .
    [4] F. Yu, C. Qing, X. Xu, B. Cai, Image and video dehazing using view-based cluster segmentation, in: Visual Communications and Image Processing (VCIP), 2016, IEEE, 2016, pp. 1–4.
    [5] I. Yoon , S. Jeong , J. Jeong , D. Seo , J. Paik , Wavelength-adaptive dehazing using histogram merging-based classification for uav images, Sensors 15 (3) (2015) 6633–6651 .
    [6] S.K. Nayar , S.G. Narasimhan , Vision in bad weather, in: IEEE International Conference on Computer Vision, 1999, pp. 820–827 . vol.2.
    [7] S.G. Narasimhan , S.K. Nayar , Contrast restoration of weather degraded images, IEEE Trans. Pattern Anal. Mach. Intell. 25 (6) (2003) 713–724 .
    [8] J.P. Tarel , N. Hautiere , Fast visibility restoration from a single color or gray level image, in: IEEE International Conference on Computer Vision, 2009, pp. 2201–2208 .
    [9] K. Nishino , L. Kratz , S. Lombardi , Bayesian defogging, Int. J. Comput. Vis. 98 (3) (2011) 263–278.
    [10] G. Meng , Y. Wang , J. Duan , S. Xiang , C. Pan , Efficient image dehazing with boundary constraint and contextual regularization, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 617–624.
    [11] J. Kopf , B. Neubert , B. Chen , M. Cohen , D. Cohen-Or , O. Deussen , M. Uytten- daele , D. Lischinski , Deep photo: model-based photograph enhancement and viewing, Acm Trans. Graph. 27 (5) (2008) 32–39 .
    [12] B. Cai , X. Xu , D. Tao , Real-time video dehazing based on spatio-temporal mrf, in: Pacific Rim Conference on Multimedia, Springer, 2016, pp. 315–325 .
    [13] G. Wang, G. Ren, L. Jiang, T. Quan, Single image dehazing algorithm based on sky region segmentation, Inf. Technol. J. 12 (6) (2013) 1168–1175.
    [14] Q. Zhu , J. Mai , L. Shao , A fast single image haze removal algorithm using color attenuation prior, IEEE Trans. Image Process. 24 (11) (2015) 3522–3533 .
    [15] W. Ren , S. Liu , H. Zhang , J. Pan , X. Cao , M.-H. Yang , Single image dehazing via multi-scale convolutional neural networks, in: European Conference on Com- puter Vision, Springer, 2016, pp. 154–169 .
    [16] B. Cai , X. Xu , K. Jia , C. Qing , D. Tao , Dehazenet: An end-to-end system for single image haze removal, IEEE Trans. Image Process. 25 (11) (2016) 5187–5198 .
    [17] K. He, J. Sun, Fast guided filter, Comput. Sci. (2015) . arXiv preprint arXiv:1505. 00996 .
    [18] K. He , J. Sun , X. Tang , Guided image filtering, IEEE Trans. Softw. Eng. 35 (6) (2013) 1397–1409 .
    [19] M. Sulami , I. Glatzer , R. Fattal , M. Werman ,Automatic recovery of the atmo- spheric light in hazy images, in: IEEE International Conference on Computa- tional Photography (ICCP), 2014, IEEE, 2014, pp. 1–11 .
    [20] A.A. Goshtasby, S. Nikolov, Image fusion: advances in the state of the art, Information Fusion 8 (2) (2007) 114-118.
    [21] N. Mitianoudis, T. Stathaki, Pixel-based and region-based image fusion schemes using ICA bases, Information Fusion 8 (2) (2007) 131–142.
    [22] H. Li, B. Manjunath, S. Mitra, Multisensory image fusion using the wavelet transform, Graphical Models and Image Processing 57 (3) (1995) 235–245.
    [23] G. Pajares, J. Cruz, A wavelet-based image fusion tutorial, Pattern Recognition 37 (9) (2004) 1855–1872.
    [24] M. Beaulieu, S. Foucher, L. Gagnon, Multi-spectral image resolution refinement using stationary wavelet transform, in: Proceedings of the International Geoscience and Remote Sensing Symposium, 1989, pp.4032–4034.
    [25] E.J. McCartney , Optics of the atmosphere: scattering by molecules and parti- cles., John Wiley and Sons, Inc., New York, 1976, p. 421.
    [26] R.T. Tan, Visibility in bad weather from a single image, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008 CVPR 2008, IEEE, 2008, pp. 1–8.
    [27] A.J. Preetham, P. Shirley, B. Smits, A practical analytic model for daylight, in: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, ACM Press/Addison Wesley Publishing Co., 1999, pp. 91–100.
    [28] Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004) 600–612.

Tapasmini Sahoo and Kunal Kumar Das (2024), An Optimized Stationary Wavelet Fusion Technique for Image de-hazing. IJEER 12(4), 1181-1187. DOI: 10.37391/IJEER.120408.