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A Content-Adaptive Method for Single Image Dehazing

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6298))

Abstract

A content adaptive method for single image dehazing is proposed in this work. Since the degradation level affected by haze is related to the depth of the scene and pixels in each specific part of the image (such as trees, buildings or other objects) tend to have similar depth to the camera, we assume that the degradation level affected by haze of each region is the same That is, the transmission in each region should be similar as well. Based on this assumption, each input image is segmented into different regions and transmission is estimated for each region followed by refinement by soft matting. As a result, the hazy images can be successfully recovered. The experimental results demonstrate that the proposed method performs satisfactorily.

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References

  1. Chavez, P.: An improved dark-object substraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24, 450–479 (1988)

    Google Scholar 

  2. Fattal, R.: Single image dehazing. In: SIGGRAPH, pp. 1–9 (2008)

    Google Scholar 

  3. 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. In: SIGGRAPH Asia (2008)

    Google Scholar 

  4. Levin, A., Lischinski, D., Weiss, Y.: A closed form solution to natural image matting. In: CVPR, vol. 1, pp. 61–68 (2006)

    Google Scholar 

  5. Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: CVPR, pp. 598–605 (2000)

    Google Scholar 

  6. Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. IJCV 48, 233–254 (2002)

    Article  MATH  Google Scholar 

  7. Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. PAMI 25, 713–724 (2003)

    Google Scholar 

  8. Narasimhan, S.G., Nayar, S.K.: Interactive deweathering of an image using physical models. In: Workshop on Color and Photometric Methods in Computer Vision (2003)

    Google Scholar 

  9. Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: ICCV, p. 820 (1999)

    Google Scholar 

  10. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: CVPR, vol. 1, p. 325 (2001)

    Google Scholar 

  11. Shwartz, S., Namer, E., Schechner, Y.Y.: Blind haze separation. In: CVPR, vol. 2, pp. 1984–1991 (2006)

    Google Scholar 

  12. Tan, R.: Visibility in bad weather from a single image. In: CVPR (2008)

    Google Scholar 

  13. He, K., Sun, J., Tang, X.: Single Image Haze Removal Using Dark Channel Prior. In: CVPR (2009)

    Google Scholar 

  14. Oakley, J.P., Bu, H.: Correction of simple contrast loss in color images. IEEE Transactions on Image Processing 16(2), 511–522 (2007)

    Article  MathSciNet  Google Scholar 

  15. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)

    Article  Google Scholar 

  16. Chen, X., Yan, X., Chu, X.: Fast Algorithms for Foggy Image Enhancement Based on Convolution. In: International Symposium on Computational Intelligence and Design (ISCID), October 2008, vol. 2(17) pp. 165–168 (2008)

    Google Scholar 

  17. Kim, D., Jeon, C., Kang, B., Ko, H.: Enhancement of Image Degraded by Fog Using Cost Function Based on Human Visual Model. In: Multisensor Fusion and Integration for Intelligent Systems (MFI), August 20-22, pp. 64–67 (2008)

    Google Scholar 

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Chu, CT., Lee, MS. (2010). A Content-Adaptive Method for Single Image Dehazing. In: Qiu, G., Lam, K.M., Kiya, H., Xue, XY., Kuo, CC.J., Lew, M.S. (eds) Advances in Multimedia Information Processing - PCM 2010. PCM 2010. Lecture Notes in Computer Science, vol 6298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15696-0_33

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  • DOI: https://doi.org/10.1007/978-3-642-15696-0_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15695-3

  • Online ISBN: 978-3-642-15696-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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