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Tetrolet Transform and Dual Dictionary Learning-Based Single Image Fog Removal

  • Research Article-Computer Engineering and Computer Science
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Abstract

The removal of fog or haze from video frames and images has been a major focus in the area of computer vision since fog has a detrimental effect on monitoring and surveillance systems, as well as on the recognition of scene objects and other applications. Numerous defogging strategies have been presented thus far, including those based on the “colour-line model”, polarization, “anisotropic diffusion”, and the “dark channel prior” (DCP). Nevertheless, when the scene counters a thick fog and sky regions, these approaches fail to provide high-quality output. The authors suggest a novel haze/fog removal approach that uses tetrolet transformation to decompose a foggy image into low- and high-frequency components based on their structural information and dual dictionary learning-based residual frequency extractor to extract additional residual image information. DCP operation is performed on the low-frequency component to recover more fog-free information while sharpening the tetrolet coefficients extracts finer details. The inverse transformed image is then added to the residual high-frequency image component and post-processed using contrast limited adaptive histogram equalization to balance the contrast. Lastly, S and V channel gain regulator optimizes the contrast-enhanced image's colour and intensity. Compared to current methodologies, the suggested method significantly improves the overall picture quality. Quantitative and qualitative data support the statements.

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References

  1. He, K.; Sun, J.; Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2341–2353 (2011). https://doi.org/10.1109/tpami.2010.168

    Article  Google Scholar 

  2. Tarel J.P., Hautiere N.: Fast visibility restoration from a single color or gray level image. in: Proc. 12th IEEE international conference on computer vision. pp. 2201–2208(2009). https://doi.org/10.1109/ICCV.2009.5459251

  3. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K: Instant dehazing of images using polarization. Computer Vision and Pattern Recognition, in: Proc. IEEE Computer Society Conference. pp. 1–325(2001). https://doi.org/10.1109/CVPR.2001.990493

  4. Kopf, J.; Neubert, B.; Chen, B.; Cohen, M.; Cohen-Or, D.; Deussen, O.; Uyttendaele, M.; Lischinski, D.: Deep photo: model-based photograph enhancement and viewing. ACM Trans. Graph. 27(5), 116:1-116:10 (2008)

    Article  Google Scholar 

  5. Anwar, M.I.; Khosla, A.: Vision enhancement through single image fog removal. Eng. Sci. Technol. Int. J. 20(3), 1075–1083 (2017). https://doi.org/10.1016/j.jestch.2021.03.014

    Article  Google Scholar 

  6. Kyungil, K.; Soohyun, K.; Kyung-Soo, K.: Effective image enhancement techniques for fog-affected indoor and outdoor images. IET Image Proc. 12, 465–471 (2018). https://doi.org/10.1049/iet-ipr.2016.0819

    Article  Google Scholar 

  7. Tripathi, A.K.; Mukhopadhyay, S.: Single image fog removal using anisotropic diffusion. IET Image Process. 6(7), 966 (2012). https://doi.org/10.1049/iet-ipr.2011.0472

    Article  MathSciNet  Google Scholar 

  8. Fattal, R.: Dehazing using color-lines. ACM Trans. Graph 34(1), 13 (2014). https://doi.org/10.1145/2651362

    Article  Google Scholar 

  9. Xiao, C.; Gan, J.: Fast image dehazing using guided joint bilateral filter. Vis Comput. 28, 713–721 (2012)

    Article  Google Scholar 

  10. Hu, D.; Yang, Y.; Li, B.; Tang, H.; Xu, Y.: Fast outdoor hazy image dehazing based on saturation and brightness. IET Image Process. 16, 900–912 (2022). https://doi.org/10.1049/ipr2.12396

    Article  Google Scholar 

  11. Chen, T.; Liu, M.; Gao, T.; Cheng, P.; Mei, S.; Li, Y.: A fusion-based defogging algorithm. Remote Sens. 14, 425–445 (2022)

    Article  Google Scholar 

  12. Wang, Y.; Fan, C.: Single image defogging by multiscale depth fusion. IEEE Trans. Image Process. 23(11), 4826–4837 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  13. Tan R.: Visibility in bad weather from a single image. Proc.IEEE Conf. Computer Vision and Pattern Recognition, June (2008). https://doi.org/10.1109/CVPR.2008.4587643

  14. Cai, B.; Xu, X.; Jia, K.; Qing, C.; Dacheng, T.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  15. Zhao, S.; Zhang, L.; Shen, Y.; Zhou, Y.: RefineDNet: A weakly supervised refinement framework for single image dehazing. IEEE Trans. Image Process. 30, 3391–3404 (2021)

    Article  Google Scholar 

  16. Yin, J.; Huang, Y.; Chen, B.; Ye, S.: Color transferred convolutional neural networks for image dehazing. IEEE Trans. Circuits Syst. Video Technol. 30(11), 3957–3967 (2020)

    Article  Google Scholar 

  17. Ray, A.: 7 Limitations of deep learning algorithms of AI. www.amitray.com. https://amitray.com/7-limitations-of-deep-learning-algorithms-of-ai/ Accessed 08 December 2022

  18. Joy, A.: Pros and cons of deep learning. Pythonista planet. (2022, April 28). https://pythonistaplanet.com/pros-and-cons-of-deep-learning/ Accessed 08 December 2022

  19. Yuille, A.L.; Liu, C.: Deep nets: What have they ever done for vision? Int. J. Comput. Vision 129(3), 781–802 (2020). https://doi.org/10.1007/s11263-020-01405-z

    Article  Google Scholar 

  20. Krommweh, J.: Tetrolet transform: a new adaptive Haar wavelet algorithm for sparse image representation. J. Vis. Commun. Image Represent. 21(4), 364–374 (2010). https://doi.org/10.1016/j.jvcir.2010.02.011

    Article  Google Scholar 

  21. He K., Sun J. and Tang X.: Guided image filtering. In Computer Vision—ECCV 2010, vol. 6311, pp. 1–14 (2010)

  22. Jadwiga, R.; Kendall, P.; Donald, S.: Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs. IEEE Trans. Biomed. Eng. 35(10), 817–827 (1988)

    Article  Google Scholar 

  23. Ying. L., Tek, Ming., Ng Beng Keat, L.: A wavelet based image sharpening algorithm. In: International conference on computer science and software engineering . pp 1053–1056 (2008). https://doi.org/10.1109/CSSE.2008.1631

  24. Polesel, A.; Ramponi, G.; John, M.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000). https://doi.org/10.1109/83.826787

    Article  Google Scholar 

  25. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphic gems, pp. 474–485. Elsevier (1994)

    Chapter  Google Scholar 

  26. Economopoulos, T.L.; Asvestas, P.A.; Matsopoulos, G.K.: Contrast enhancement of images using partitioned iterated function systems. Image Vis. Comput. 28(1), 45–54 (2010)

    Article  Google Scholar 

  27. Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. PAMI 7(11), 674–693 (1989)

    Article  MATH  Google Scholar 

  28. Hajjaji M. A., Albouchi A., & Mtibaa A.: Combining DWT/KLT for secure transfer of color images. 2019 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) (2019). https://doi.org/10.1109/dtss.2019.8914838

  29. Ajili S., Hajjaji M. A., & Mtibaa A.: Hybrid SVD-DWT watermarking technique using AES algorithm for Medical Image Safe Transfer. 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) (2015). https://doi.org/10.1109/sta.2015.7505164

  30. Hajjaji M. A., Gafsi M., Mtibaa A.: Discrete cosine transform space for hiding patient information in the medical images. 2019 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) (2019). https://doi.org/10.1109/dtss.2019.8914880

  31. Tomasi C., Manduchi R.: Bilateral filtering for gray and color images. Proc. Sixth IEEE Int’l Conference Computer Vision, p. 839 (1998)

  32. Aharon, M.; Elad, M.; Bruckstein, A.: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006). https://doi.org/10.1109/tsp.2006.881199

    Article  MATH  Google Scholar 

  33. Abdullah-Al-Wadud, M.; Kabir, Md.; Akber Dewan, M.; Chae, O.: A dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consumer Electron. 53(2), 593–600 (2007). https://doi.org/10.1109/TCE.2007.381734

    Article  Google Scholar 

  34. Xu Jia., Doo Choi Byeong., Jung Seung-Won., Ko Sung-Jea : Image enhancement using two gamma-rescaling curves and multiscale Gaussian matrix. in: International Conference on Intelligent and Advanced Systems. pp. 709–713 (2007).

  35. Hasler, D.; Suesstrunk, S.E.: Measuring colorfulness in natural images. Electron. Imaging Int. Soc. Optics Photonics (2003). https://doi.org/10.1117/12.477378

    Article  Google Scholar 

  36. Gabarda, S.; Cristóbal, G.: Image quality assessment through a logarithmic anisotropic measure. SPIE Photonics Europe Strasbourg (France) (2008). https://doi.org/10.1117/12.781370

    Article  Google Scholar 

  37. Callico, G.M.; Lopez, S.; Gabarda, S.; Gil, E.; Cristobal, G.; Lopez, J.; Sarmiento, R.: Anisotropic quality measurement applied to H.264 video compression. SPIE VLSI Circuits Syst. (2009). https://doi.org/10.1117/12.821652

    Article  Google Scholar 

  38. Gabarda, S.; Cristóbal, G.: Blind image quality assessment through Anisotropy. J. Opt. Soc. Am. A. 24, B42–B51 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Haldia Institute of Technology, Haldia, 721657, India

    Manas Sarkar

  2. Department of Electronics and Instrumentation Engineering, Haldia Institute of Technology, Haldia, 721657, India

    Priyanka Sarkar Rakshit

  3. Department of Applied Physics, University of Calcutta, Kolkata, 700009, India

    Ujjwal Mondal

  4. Department of Computer Science and Engineering, National Institute of Technology, Durgapur, 713209, India

    Debashis Nandi

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  1. Manas Sarkar
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Correspondence to Manas Sarkar.

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Sarkar, M., Sarkar Rakshit, P., Mondal, U. et al. Tetrolet Transform and Dual Dictionary Learning-Based Single Image Fog Removal. Arab J Sci Eng 48, 10771–10786 (2023). https://doi.org/10.1007/s13369-023-07681-4

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  • DOI: https://doi.org/10.1007/s13369-023-07681-4

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