CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Multistage Sub-Image Filtering Technique with Improved De-noising Feature for Road Detection in Disaster Management

by Md. Abdul Alim Sheikh, Sumitra Mukhopadhyay
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 22
Year of Publication: 2015
Authors: Md. Abdul Alim Sheikh, Sumitra Mukhopadhyay
10.5120/21829-5086

Md. Abdul Alim Sheikh, Sumitra Mukhopadhyay . Multistage Sub-Image Filtering Technique with Improved De-noising Feature for Road Detection in Disaster Management. International Journal of Computer Applications. 121, 22 ( July 2015), 1-8. DOI=10.5120/21829-5086

@article{ 10.5120/21829-5086,
author = { Md. Abdul Alim Sheikh, Sumitra Mukhopadhyay },
title = { Multistage Sub-Image Filtering Technique with Improved De-noising Feature for Road Detection in Disaster Management },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 22 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number22/21829-5086/ },
doi = { 10.5120/21829-5086 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:05.849533+05:30
%A Md. Abdul Alim Sheikh
%A Sumitra Mukhopadhyay
%T Multistage Sub-Image Filtering Technique with Improved De-noising Feature for Road Detection in Disaster Management
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 22
%P 1-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to rapid urban development, the Geographic Information System (GIS) database needs to be updated with timely and accurate road network information. This paper presents an approach to design a module for image pre-treatment of roads (or roads seeds) and help to decide the most suitable emergency transportation route in disastrous area. Also, in such situation, the quality of the image may degrade during capture or transmission as the entire process becomes prone to noise and instability. Therefore for any kind of information processing or decision making image pre-treatment is a significant part. This paper presents a Multistage Hybrid Median filtering (MHMF) technique to significantly improve noise reduction performance of satellite/aerial road images while preserving the integrity of edge and detail information. Further, the images are divided into subparts and they are processed using the proposed MHMF. Then the two filtered sub-images are combined and we can improve overall performance even further. To support the above claim, a case study has been carried out on two recent natural disasters happened in India along with other benchmark problems and the studies show the effectiveness of the proposed system in real environment.

References
  1. S. Voigt, T. Kemper, T. Riedlinger, R. Kiefl, K. Scholte, and H. Mehl, "Satellite image analysis for disaster and crisis-management support," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, pp. 1520-1528, June 2007.
  2. George S. Percivall, III, Senior Member, IEEE, Nadine S. Alameh, Hervé Caumont, Karen L. Moe, and John D. Evans, "Improving Disaster Management Using Earth Observations—GEOSS and CEOS Activities" IEEE journal of selected topics in applied earth observations and remote sensing, vol. 6, no. 3, June 2013
  3. M. D. YANG, T. C. SU, C. H. HSU, K. C. CHANG and A. M. WU, "Mapping of the 26 December 2004 tsunami disaster by using FORMOSAT-2 images," International Journal of Remote Sensing, Vol. 28, Nos. 13–14, July 2007, 3071–3091
  4. http://www. nrsc. gov. in
  5. H. Faraji and W. J. MacLean, "CCD noise removal in digital images," IEEE Transactions on image processing, vol. 15, 2006, pp. 2676–2685
  6. Md. Abdul Alim sheikh, S. Mukhopadhyay, "Comparative Analysis of Noise Reduction Techniques for Image Enhancement ", IJIRD, PP: 103- 112, volume 2 Issue 12, December 2013.
  7. A Chambolle. et al. , (1998) "Nonlinear Wavelet Image Processing: Variational Problems, Compression and Noise Removal through Wavelet Shrinkage", IEEE Trans. Image Processing, 7, pp. 319-335.
  8. J. F. Abramatic and L. M. Silverman, "Nonlinear restoration of noisy images," IEEE Trans. Pattern Anal. Mach. Intell. , vol. PAMI-4, pp. 141-149, Mar. 1982.
  9. D. L. Donoho, I. M. Johnstone, (1994) "Ideal Spatial Adaptation by Wavelet Shrinkage," Biometrika, 81, No. 3, pp. 425–455.
  10. S. Mallat, (1989) "A Theory for Multiresolution Signal Decomposition: the Wavelet Representation," IEEE Trans. on Patt. Anal. Mach Intell. , 11, pp. 674-693.
  11. P. Perona and J. Malik, (1990) "Scale-space and Edge Detection using Anisotropic Diffusion," IEEE Trans. Pattern Analysis and Machine Intelligence, 12, No. 7, pp. 629–639.
  12. C. Tomasi and R. Manduchi, "Bilateral Filtering forGray and Color Images," in Proc. Int. Conf. ComputerVision, pp. 839–846, 1998.
  13. D. L. Donoho, (1995) "De-noising by Soft Thresholding," IEEE Trans. on Inform, Theory, 41, No. 3, pp. 613- 627.
  14. S G Chang, B. Yu, and M. Vetterli,"Adaptive Wavelet Thresholding for Image Denoising and Compression," IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532 –1546, September 2000.
  15. N. C. Gallagher,Jr. and G. L. Wise, "A Theoretical Analysis of the Properties of Median Filters," IEEE Trans. Acoust. , Speech and Signal Processing, vol. ASSP-29, pp. 1136-1141, Dec. 1981.
  16. D. R. K. Brownrigg, "The weighted median filter," Commun. ACM, vol. 27, no. 8, pp. 807-818, Aug. 1984.
  17. A. Nieminen, P. Heinonen, and Y. Neuvo, "A new class of detail preserving filters for image processing," IEEE Trans. Pattern Anal. Mach. Intell. , vol. PAMI-9, pp. 74-90, Jan. 1987.
  18. P. Heinonen and Y. Neuvo, "FIR-median hybrid filters," IEEE Trans. Acoust. , Speech, Signal Processing, vol. ASSP-35, pp. 832-838, June 1987.
  19. Zhang, S. , and Karim, M. A. : 'A new impulse detector for switching median filters', IEEE Signal Process. Lett. , 2002, 9, (11), pp. 360–363.
  20. H Hwang and R A Haddad, "Adaptive Median Filter: New Algorithms and Results", IEEE Transactions on image processing Vol 4 No 4 April 1995
  21. H. -M. Lin and A. N. Willson, Jr. , "Median filters with adaptive length," IEEE Trans. Circuits Syst. , vol. CAS-35. pp. 675-690, June 1988.
  22. R. Ding and A. N. Venetsanopoulos, "Generalized homomorphic and adaptive order statistic filters for the removal of impulsive and signal-dependent noise," IEEE Trans. Circuits Syst . , vol. CAS-34, pp. 948-955, Aug. 1987.
  23. L. Lin , R. Yang, M. Gabbouj and Y. Neuvo, "Weighted Median Filters: A Tutorial," IEEE Transactions On Circuits And Systems-II: Analog And Digital Signal Processing, VOL. 43, NO. 3, MARCH 1996
  24. Sung-Jea Ko and Y. H. Lee, "Centre Weighted Median Filters and Their Applications to image Enhancement," IEEE Transactions on Circuits and Systems, Vol. 38, No. 9, September 1991.
  25. C. L. Mallows, "Some theory of non-linear smoothers," The Annals of Statistics, vol. 8, pp. 695-715, 1980.
  26. Zhou Wang and David Zhang, "Progressive Switching Median Filter for the Removal of Impulse noise from Highly Corrupted Images," IEEE Transaction on circuits and systems-II: analog and digital signal processing Vol. 46 No 1 January 1999.
  27. T. Chen, K. -K. Ma, and L. -H. Chen, "Tri-state median filter for image denoising," IEEE Transactions on Image Processing, vol. 8, no. 12, pp. 1834–1838, 1999
  28. D. A. F. Florencio and R. W. Schafer, "Decision-based median filter using local signal statistics," in Visual Communications and Image Processing '94, vol. 2308 of Proceedings of SPIE, pp. 268–275, Chicago, Ill, USA, September 1994
  29. Abdessamad ben hamza, Pedro l luque-escamilla, Jos´e mart´inez-aroza, and Ramon roman-roldan, "Removing Noise and Preserving Details with Relaxed Median Filters," Journal of Mathematical Imaging and Vision 11, 161–177 (1999) Kluwer Academic Publishers. Manufactured in the Netherlands.
  30. G. R. Arce and R. E. Foster, "Detail preserving ranked-order based filters for image processing," IEEE Trans. Acoust . . Speech,Signal Processing, vol. 37, pp. 83-98, Jan. 1989.
  31. Zhang, D. S. ; Kouri, OJ. "Varying weight trimmed mean filter for the restoration of impulse noise corrupted images", Acoustics, Speech, and Signal Processing IEEE International Conference. vol. 4. pp. 137-140. 2005.
  32. J. S. Lee, "Digital image enhancement and noise filtering by use of local statistics," IEEE Trans. Pattern Anal. Mach. Intell. , vol. PAMI-2, pp. 165-168, Mar. 1980.
  33. Sun, T. , and Neuvo, Y. : 'Detail-preserving median based filters in image processing', Pattern Recognit. Lett. , 1994, 15, (4), pp. 341–347
  34. J. Najeer Ahamed and V. Rajamani, "Design of Hybrid Filter for Denoising Images Using Fuzzy Network and Edge Detecting ," American Journal of Scientific Research ISSN 1450-223X Issue 3(2009), pp. 5-14.
  35. F. Russo and G. Ramponi, "A fuzzy operator for the enhancement of blurred and noisy images," IEEE Trans. Image Processing, vol. 4, pp. 1169–1174, Aug. 1995.
  36. C. -S. Lee, Y. -H. Kuo, and P. -T. Yu, "Weighted fuzzy mean filters for image processing," Fuzzy Sets Syst. , no. 89, pp. 157–180, 1997.
  37. C. S. Lee, Y. H. Kuo, Adaptive fuzzy filter and its application to image enhancement, in: E. E. Kerre, M. Nachtegael (Eds. ), Fuzzy Techniques in Image Processing, Springer, New York, 2000, pp. 172–193.
  38. J. H. Wang, W. J. Liu, L. D. Lin, "An histogram-based fuzzy filter for image restoration," IEEE Trans. Systems Man and Cybernetics Part BCybernetics 32 (2) (2002) 230–238.
  39. H. Xu, G. Zhu, H. Peng, D. Wang, Adaptive fuzzy switching filter for images corrupted by impulse noise, Pattern Recognition Lett. 25 (2004)1657–1663.
  40. Dimitri Van De Ville, Mike Nachtegael, Dietrich Van der Weken, Etienne E. Kerre, Wilfried Philips, IEEE, and Ignace Lemahieu "Noise Reduction by Fuzzy Image Filtering", IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 11, NO. 4, AUGUST 2003
  41. Ahmet M. Eskicioglu, Paul S. Fisher, "Image Quality Measures and Their Performance" IEEE Transactions on Communication, Vol. 43, No. 12, pp. 2959-2965, December 1995.
  42. Zhou Wang, Alan C. Bovik, "A Universal Image Quality Index", IEEE SIGNAL PROCESSING LETTERS, VOL. 9, NO. 3, MARCH 2002
  43. K. Panetta,Y. Zhou, S. Agaian and H. Jia. "Nonlinear Unsharp Masking for Mammogram Enhancement". IEEE Transactions on Information Technology in Biomedicine, 2011, 15(6): 918-928.
Index Terms

Computer Science
Information Sciences

Keywords

Image Preprocessing Sub-image Median Filter Road Detection GIS Objective Image Quality Metrics Signal to Noise Ratio Image Quality Index.

Powered by PhDFocusTM