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Image Clarification Method Based on Structure-Texture Decomposition with Texture Refinement

  • Masato TodaEmail author
  • Kenta Senzaki
  • Masato Tsukada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)

Abstract

This paper presents a high quality and low complexity image clarification method, which restores the visibility of images captured in bad weather and poor lighting conditions. A sequential processing of conventional dehazing and backlit correction methods has a problem that textures and noises are overemphasized by the corrections. The proposed method first decomposes a captured image into two components: a structure component forming smooth regions and strong edges and a rest component for fine textures and noises. Image enhancement is conducted based on analyses of the first component, while controlling an amplification factor of the texture component. The utilization of the structure component for the enhancement enables pixel-wise corrections without local area analysis which results in lower computational cost. Experimental results demonstrate that the proposed method can successfully enhance image qualities and its computational cost is reasonable for real-time video processing.

Keywords

Video surveillance Image clarification Image dehazing Backlit correction 

References

  1. 1.
    Chan, T.F., Osher, S., Shen, J.: The digial TV filter and nonlinear denoising. IEEE Transactions on Image Processing 10(2), 231–241 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Fattal, R.: Single image dehazing. In: Proc. ACM SIGGRAPH 2008, pp. 1–9 (2008)Google Scholar
  3. 3.
    Huang, S.-C., Chen, B.-H., Wang, W.-J.: Visibility restoration of single hazy images captured in real-world weather conditions. IEEE Transactions on Circuits and Systems for Video Technology 24(10), 1814–1824 (2014)CrossRefGoogle Scholar
  4. 4.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR) 2009, pp. 1956–1963 (2009)Google Scholar
  5. 5.
    Jobson, D.J., Rahman, Z.-U., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing 6(7), 965–976 (1997)CrossRefGoogle Scholar
  6. 6.
    Kim, J.-H, Jang, W.-D., Park, Y., Lee, D.-H., Sim, J.-Y., Kim, C.-S.: Temporally x real-time video dehazing. In: 19th IEEE International Conference on Image Processing, ICIP 2012, pp. 969–972 (2012)Google Scholar
  7. 7.
    Long, J., Shi, Z., Tang, W., Zhang, C.: Single remote sensing image dehazing. IEEE Geoscience and Remote Sensing Letters 11(1), 59–63 (2014)CrossRefGoogle Scholar
  8. 8.
    Li, B., Wang, S., Zheng, J., Zheng, L.: Single image haze removal using content-adaptive dark channel and post enhancement. IET Computer Vision 8(2), 131–140 (2014)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 617–624 (2013)Google Scholar
  10. 10.
    Monobe, Y., Yamashita, H., Kurosawa, T., Kotera, H.: High dynamic range compression for digital video camera using local contrast enhancement. In: International Conference on Consumer Electronics (ICCE) 2015, Digest of Technical Papers, pp. 217–218 (2005)Google Scholar
  11. 11.
    Narasimhan, S.G., Nayer, S.K.: Vision and the Atmosphere. International Journal on Computer Vision 48(3), 233–254 (2002)CrossRefzbMATHGoogle Scholar
  12. 12.
    Narasimhan, S.G., Nayer, S.K.: Contrast Restoration of Weather Degraded Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(6), 713–724 (2003)CrossRefGoogle Scholar
  13. 13.
    Oren, M., Nayer, S.K.: Generalization of lmbert’s reflectance model. In: ACM SIGGRAPH 1994, pp. 239–246 (1994)Google Scholar
  14. 14.
    Phong, B.T.: Illumination for computer generated pictures. Commun. ACM 18(6), 311–377 (1975)CrossRefGoogle Scholar
  15. 15.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1–4), 259–268 (1992)CrossRefzbMATHGoogle Scholar
  16. 16.
    Shimoyama, S., Igarashi, M., Ikebe, M., Motohisa, J.: Local adaptive tone mapping with composite multiple gamma functions. In: 16th IEEE International Conference on Image Processing (ICIP 2009), pp. 3153–3156 (2009)Google Scholar
  17. 17.
    Schechner, Y.Y., Narasimhan, S.G., Nayer, S.K.: Polarization-Based Vision through Haze. Applied Optics, Special issue 42(3), 511–525 (2009)Google Scholar
  18. 18.
    Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2008, pp. 1–8 (2008)Google Scholar
  19. 19.
    Toda, M., Tsukada, M.: High dynamic range rendering method for YUV images with global luminance correction. In: IEEE International Conference on Consumer Electronics (ICCE) 2011, pp. 255–256 (2011)Google Scholar
  20. 20.
    Toda, M., Tsukada, M., Inoue, A., Suzuki, T.: High dynamic range rendering for YUV images with a constraint on perceptual chroma preservation. In: 16th IEEE International Conference on Image Processing (ICIP 2009), pp. 1817–1820 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.NEC CorporationKawasakiJapan

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