Home » Rain Streaks Removal in Image via Decomposition and Visibility Feature Saturation

Rain Streaks Removal in Image via Decomposition and Visibility Feature Saturation

Advertisements

Ruchi Katre

katre.ruchi9@gmail.com

Nitesh Dodkey

Surabhi Group of Institution Bhopal, India

Abstract

The rain like streaks or spots in any gray or colour image may degrade its quality.  Several removals techniques are available to remove by using high frequency components which may increase unnecessary intensity. In this paper we are comparing the rain removal algorithm by traditional decomposition methods with visibility features extraction methodology. The frequently used saturation-visibility feature (SVF) rain and removal mainly used orientation filter with digitally controlled high pass filter or HPF.  The visual depth guide or VDG may by help of the common morphological features. The high-frequency fragments, histogram of adjusted grades, popular Eigen colour, strength of ground are necessary for extract rain are discussed in this paper. Different experimental results are compared the efficacy of the rain removal algorithms and the result shows that VDG is better than the SVF method but required more computational time.

Keywords

Image decomposition method, Morphological component analysis, Raindrops omitting process, Rain removal methodology, sparse representation process, Saturation feature extraction.

pdf-1Download Full PDF

Cited as

Ruchi Katre and Nitesh Dodkey, “Rain Streaks Removal in Image via Decomposition and Visibility Feature Saturation,” International Journal of Advanced Engineering and Management, Vol. 2, No. 4, pp. 82-85,  2017.

References

  1. 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.
  2. Garg, K., & Nayar, S. K. (2004, June). Detection and removal of rain from videos. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on (Vol. 1, pp. I-I). IEEE.
  3. Garg, K., & Nayar, S. K. (2005, October). When does a camera see rain?. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on (Vol. 2, pp. 1067-1074). IEEE.
  4. Tripathi, A. K., & Mukhopadhyay, S. (2012). Removal of fog from images: A review. IETE Technical Review, 29(2), 148-156.
  5. Zhang, X., Li, H., Qi, Y., Leow, W. K., & Ng, T. K. (2006, July). Rain removal in video by combining temporal and chromatic properties. In Multimedia and Expo, 2006 IEEE International Conference on (pp. 461-464). IEEE.
  6. Ghanbari, M., Majdi, M., & Talouki, M. (2017). Video Inpainting Using a Contour-based Method in Presence of More than One Moving Objects. International Journal of Advanced Engineering and Management, 2(2), 37-44.
  7. 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 computer vision, 93(3), 348-367.
  8. Roser, M., & Geiger, A. (2009, September). Video-based raindrop detection for improved image registration. In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on (pp. 570-577). IEEE.
  9. Halimeh, J. C., & Roser, M. (2009, June). Raindrop detection on car windshields using geometric-photometric environment construction and intensity-based correlation. In Intelligent Vehicles Symposium, 2009 IEEE (pp. 610-615). IEEE.
  10. Fadili, M. J., Starck, J. L., Bobin, J., & Moudden, Y. (2010). Image decomposition and separation using sparse representations: An overview. Proceedings of the IEEE, 98(6), 983-994.
  11. Starck, J. L., Elad, M., & Donoho, D. L. (2005). Image decomposition via the combination of sparse representations and a variational approach. IEEE transactions on image processing, 14(10), 1570-1582.
  12. Mallat, S. G., & Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Transactions on signal processing, 41(12), 3397-3415.
  13. Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on information theory, 52(4), 1289-1306.
  14. Kang, L. W., Lin, C. W., & Fu, Y. H. (2012). Automatic single-image-based rain streaks removal via image decomposition. IEEE Transactions on Image Processing, 21(4), 1742-1755.
  15. Pei, S. C., Tsai, Y. T., & Lee, C. Y. (2014, July). Removing rain and snow in a single image using saturation and visibility features. In Multimedia and Expo Workshops (ICMEW), 2014 IEEE International Conference on (pp. 1-6). IEEE.
  16. Chen, D. Y., Chen, C. C., & Kang, L. W. (2014). Visual depth guided color image rain streaks removal using sparse coding. IEEE transactions on circuits and systems for video technology, 24(8), 1430-1455.

steakhouse-1

Advertisements
%d bloggers like this: