Utilizing Local Phase Information to Remove Rain from Video

Abstract

In the context of extracting information from video, bad weather conditions like rain can have a detrimental effect. In this paper, a novel framework to detect and remove rain streaks from video is proposed. The first part of the proposed framework for rain removal is a technique to detect rain streaks based on phase congruency features. The variation of features from frame to frame is used to estimate the candidate rain pixels in a frame. In order to reduce the number of false candidates due to global motion, frames are registered using phase correlation. The second part of the proposed framework is a novel reconstruction technique that utilizes information from three different sources, which are intensities of the rain affected pixel, spatial neighbors, and temporal neighbors. An optimal estimate for the actual intensity of the rain affected pixel is made based on the minimization of registration error between frames. An optical flow technique using local phase information is adopted for registration. This part of the proposed framework for removing rain is modeled such that the presence of local motion will not distort the features in the reconstructed video. The proposed framework is evaluated quantitatively and qualitatively on a variety of videos with varying complexities. The effectiveness of the algorithm is quantitatively verified by computing a no-reference image quality measure on individual frames of the reconstructed video. From a variety of experiments that are performed on output videos, it is shown that the proposed technique performs better than state-of-the-art techniques.

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References

  1. Alessandrini, M., Basarab, A., Liebgott, H., & Bernard, O. (2013). Myocardial motion estimation from medical images using the monogenic signal. IEEE Transactions on Image Processing, 22(3), 1084–1095.

    Article  MathSciNet  Google Scholar 

  2. Barnum, P. C., Narasimhan, S., & Kanade, T. (2010). Analysis of rain and snow in frequency space. International Journal of Computer Vision, 86(2–3), 256–274.

    Article  Google Scholar 

  3. Bossu, J., Hautière, N., & Tarel, J. P. (2011). Rain or snow detection in image sequences through use of a histogram of orientation of streaks. International Journal of Comput Vision, 93(3), 348–367.

    Article  Google Scholar 

  4. Brewer, N. (2008). Using the shape characteristics of rain to identify and remove rain from video. Structural, syntactic, and statistical pattern recognition (pp. 451–458). Berlin: Springer.

    Google Scholar 

  5. Felsberg, M. (2007). Optical flow estimation from monogenic phase. Complex motion (pp. 1–13). Berlin: Springer.

    Google Scholar 

  6. Felsberg, M., & Sommer, G. (2001). The monogenic signal. IEEE Transactions on Signal Processing, 49(12), 3136–3144. doi:10.1109/78.969520.

    Article  MathSciNet  Google Scholar 

  7. Field, D. J., et al. (1987). Relations between the statistics of natural images and the response properties of cortical cells. The Journal of the Optical Society of America A, 4(12), 2379–2394.

    Article  Google Scholar 

  8. Garg, K., & Nayar, S. (2004). Detection and removal of rain from videos. In CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, (vol. 1, pp. I-528–I-535).

  9. Garg, K., & Nayar, S. (2007). Vision and rain. International Journal of Computer Vision, 75(1), 3–27.

    Article  Google Scholar 

  10. Garg, K., & Nayar, S. K. (2003). Photometric model of a rain drop. Technical Report, New York: Columbia University.

  11. Hase, H., Miyake, K. & Yoneda, M. (1999). Real-time snowfall noise elimination. In Proceedings of International Conference on Image Processing, ICIP 99 (vol. 2, pp. 406–409).

  12. Huang, D. A., Kang, L. W., Yang, M. C., Lin, C. W., & Wang, Y. C. (2012). Context-aware single image rain removal. In 2012 IEEE International Conference on Multimedia and Expo (ICME) (pp. 164–169).

  13. Kang, L. W., Lin, C. W., & Fu, Y. H. (2012a). Automatic single-image-based rain streaks removal via image decomposition. IEEE Transactions on Image Processing, 21(4), 1742–1755.

    Article  MathSciNet  Google Scholar 

  14. Kang, L. W., Lin, C. W., Lin, C. T. & Lin, Y. C. (2012b). Self-learning-based rain streak removal for image/video. In 2012 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1871–1874).

  15. Kim, J. H., Lee, C., Sim, J. Y., & Kim, C. S. (2013). Single-image deraining using an adaptive nonlocal means filter. In 2013 20th IEEE International Conference on Image Processing (ICIP) (pp. 914–917).

  16. Kovesi, P. (1999). Image features from phase congruency. VIDERE: Journal of Computer Vision Research, 1(3), 1–26.

    Google Scholar 

  17. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  18. Mechler, F., Reich, D. S., & Victor, J. D. (2002). Detection and discrimination of relative spatial phase by v1 neurons. The Journal of Neuroscience, 22, 6129–6157.

    Google Scholar 

  19. Moorthy, A. K., & Bovik, A. C. (2010). A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5), 513–516.

    Article  Google Scholar 

  20. Morrone, M., & Owens, R. (1987). Feature detection from local energy. Pattern Recognition Letters, 6(5), 303–313.

    Article  Google Scholar 

  21. Narasimhan, S., & Nayar, S. (2002). Vision and the atmosphere. International Journal of Computer Vision, 48(3), 233–254.

    Article  MATH  Google Scholar 

  22. Oppenheim, A. V., & Lim, J. S. (1981). The importance of phase in signals. Proceedings of the IEEE, 69(5), 529–541.

    Article  Google Scholar 

  23. Park, W. J., & Lee, K. H. (2008). Rain removal using kalman filter in video. In International Conference on Smart Manufacturing Application, ICSMA 2008 (pp. 494–497).

  24. Reddy, B., & Chatterji, B. N. (1996). An fft-based technique for translation, rotation, and scale-invariant image registration. IEEE Transactions on Image Processing, 5(8), 1266–1271.

    Article  Google Scholar 

  25. Santhaseelan, V., & Asari, V. K. (2011). Phase congruency based technique for the removal of rain from video. Image analysis and recognition (pp. 30–39). Berlin: Springer.

    Google Scholar 

  26. Santhaseelan, V., & Asari, V. K. (2012). A phase space approach for detection and removal of rain in video. In IS&T/SPIE Electronic Imaging, (pp. 830,114–830,114).

  27. Shen, M., Xue, P. (2011). A fast algorithm for rain detection and removal from videos. In 2011 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1–6).

  28. Starik, S., & Werman, M. (2003). Simulation of rain in videos. ICCV texture workshop, 2, 406–409.

    Google Scholar 

  29. Tripathi, A., & Mukhopadhyay, S. (2011). A probabilistic approach for detection and removal of rain from videos. IETE Journal of Research, 57(1), 82–91.

    Article  Google Scholar 

  30. Tripathi, A., & Mukhopadhyay, S. (2012a). Removal of rain from videos: A review. Signal, Image and Video Processing, 1(8), 1–10.

    Google Scholar 

  31. Tripathi, A., & Mukhopadhyay, S. (2012b). Video post processing: low-latency spatiotemporal approach for detection and removal of rain. IET Image Processing, 6(2), 181–196.

    Article  MathSciNet  Google Scholar 

  32. Unser, M., Sage, D., & Ville, D. V. D. (2009). Multiresolution monogenic signal analysis using the riesz-laplace wavelet transform. IEEE Transactions on Image Processing, 18(11), 2402–2418.

    Article  MathSciNet  Google Scholar 

  33. Venkatesh, S. & Owens, R. (1989). An energy feature detection scheme. In IEEE International Conference on Image Processing: Conference Proceedings ICIP’89, Sep 5–8 1989, Singapore: IEEE.

  34. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.

    Article  Google Scholar 

  35. Wadhwa, N., Rubinstein, M., Durand, F. & Freeman, W. T. (2013). Phase-based video motion processing. In Proceedings SIGGRAPH on ACM Transactions Graph (vol. 32(4), pp. 80).

  36. Xue, X., Jin, X., Zhang, C. & Goto, S. (2012). Motion robust rain detection and removal from videos. In 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP) (pp. 170–174).

  37. Zhang, X., Li, H., Qi, Y., Leow, W. K. & Ng, T. K. (2006). Rain removal in video by combining temporal and chromatic properties. In 2006 IEEE International Conference on Multimedia and Expo (pp. 461–464).

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Correspondence to Varun Santhaseelan.

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Communicated by Srinivasa Narasimhan.

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Santhaseelan, V., Asari, V.K. Utilizing Local Phase Information to Remove Rain from Video. Int J Comput Vis 112, 71–89 (2015). https://doi.org/10.1007/s11263-014-0759-8

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Keywords

  • Rain removal
  • Phase congruency
  • Monogenic signal
  • Optical flow