Enhancement of UAV-Aerial Images Using Weighted Differential Evolution Algorithm

Authors

  • Ahmet Emin Karkinli  Department of Geomatic Engineering, Ni?de Ömer Halisdemir University, Nigde, TURKIYE
  • Abdusselam Kesikoglu  Tomarza MA Vocational School, Kayseri University, Kayseri, TURKIYE

DOI:

https://doi.org//10.32628/CSEIT217248

Keywords:

Image Enhancement, Evolutionary Algorithm, Weighted Differential Evolution (WDE) Algorithm, Log Transform Function

Abstract

Depending on technological developments, the use of Unmanned Aerial Vehicles (UAVs) is increasing day by day and is a valuable source of data for different applications. Generally, low-cost and lightweight non-metric digital cameras are used in UAV systems. During the data collection phase, exposure parameters such as camera shutter speed, aperture value, ISO value, and various weather and light conditions have significant effects on image quality. Image enhancement methods can be used to increase image quality in accordance with the desired purpose. In this study, image enhancement is considered as an optimization problem and Weighted Differential Evolution (WDE) Algorithm is used to solve it. The image quality is enhanced by using an objective function in which performance measures of entropy value, sum of edge density and number of edge pixel are maximized. In the proposed color image enhancement method, aerial images defined in RGB color space are transformed into HSV color space images. the brightness component (V) of HSV color space is modified for image improvement with WDE algorithm. The performance of the proposed method has been compared with other existing techniques such as histogram equalization, linear contrast stretching and evolutionary computing-based image enhancement method like Artificial Bee Colony (ABC) Algorithm in terms of fitness value and image quality.

References

  1. P. Burdziakowski, "Uav In Todays Photogrammetry–Application Areas And Challenges," International Multidisciplinary Scientific GeoConference: SGEM, vol. 18, no. 2.3, pp. 241-248, 2018.
  2. M. Sauerbier and H. Eisenbeiss, "UAVs for the documentation of archaeological excavations," International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 38, no. 5, pp. 526-531, 2010.
  3. M. Aljehani and M. Inoue, "Performance evaluation of multi-UAV system in post-disaster application: Validated by HITL simulator," IEEE Access, vol. 7, pp. 64386-64400, 2019.
  4. S. D'Oleire-Oltmanns, I. Marzolff, K. D. Peter, and J. B. Ries, "Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco," Remote Sensing, vol. 4, no. 11, pp. 3390-3416, 2012.
  5. B. Shi and C. Liu, "UAV for landslide mapping and deformation analysis," in International Conference on Intelligent Earth Observing and Applications 2015, 2015, vol. 9808, p. 98080P: International Society for Optics and Photonics.
  6. A. Ulvi̇, "Analysis of the utility of the unmanned aerial vehicle (Uav) in volume calculation by using photogrammetric techniques," International Journal of Engineering and Geosciences, vol. 3, no. 2, pp. 43-49, 2018.
  7. A. Rango and A. Laliberte, "Impact of flight regulations on effective use of unmanned aircraft systems for natural resources applications," Journal of Applied Remote Sensing, vol. 4, no. 1, p. 043539, 2010.
  8. C. Amrullah, D. Suwardhi, and I. Meilano, "Product accuracy effect of oblique and vertical non-metric digital camera utilization in UAV-photogrammetry to determine fault plane," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 3, p. 41, 2016.
  9. M. Kedzierski, D. Wierzbicki, A. Sekrecka, A. Fryskowska, P. Walczykowski, and J. Siewert, "Influence of lower atmosphere on the radiometric quality of unmanned aerial vehicle imagery," Remote Sensing, vol. 11, no. 10, p. 1214, 2019.
  10. B. Chitradevi and P. Srimathi, "An overview on image processing techniques," International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 11, pp. 6466-6472, 2014.
  11. R. Maini and H. Aggarwal, "A comprehensive review of image enhancement techniques," arXiv preprint arXiv:1003.4053, 2010.
  12. S. M. Pizer et al., "Adaptive histogram equalization and its variations," Computer vision, graphics, and image processing, vol. 39, no. 3, pp. 355-368, 1987.
  13. C.-C. Yang, "Image enhancement by modified contrast-stretching manipulation," Optics & Laser Technology, vol. 38, no. 3, pp. 196-201, 2006.
  14. A. Gorai and A. Ghosh, "Gray-level image enhancement by particle swarm optimization," in 2009 world congress on nature & biologically inspired computing (NaBIC), 2009, pp. 72-77: IEEE.
  15. S. S. Agaian, K. Panetta, and A. M. Grigoryan, "Transform-based image enhancement algorithms with performance measure," IEEE Transactions on image processing, vol. 10, no. 3, pp. 367-382, 2001.
  16. J.-B. Martens and L. Meesters, "Image dissimilarity," Signal processing, vol. 70, no. 3, pp. 155-176, 1998.
  17. A. S. Ashour, S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, and A. E. Hassanien, "Computed tomography image enhancement using cuckoo search: a log transform based approach," Journal of Signal and Information Processing, vol. 6, no. 03, p. 244, 2015.
  18. M.-S. Shyu and J.-J. Leou, "A genetic algorithm approach to color image enhancement," Pattern Recognition, vol. 31, no. 7, pp. 871-880, 1998.
  19. L. dos Santos Coelho, J. G. Sauer, and M. Rudek, "Differential evolution optimization combined with chaotic sequences for image contrast enhancement," Chaos, solitons & fractals, vol. 42, no. 1, pp. 522-529, 2009.
  20. F. Katircioğlu and Z. Cingiz, "A Novel Gray Image Enhancement Using the Regional Similarity Transformation Function and Dragonfly Algorithm," El-Cezeri Journal of Science and Engineering, vol. 7, no. 3, pp. 1201-1219, 2020.
  21. K. Gupta and A. Gupta, "Image enhancement using ant colony optimization," IOSR Journal of VLSI and Signal Processing, vol. 1, no. 3, pp. 38-45, 2012.
  22. Z. Ye, M. Wang, Z. Hu, and W. Liu, "An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm," Computational intelligence and neuroscience, vol. 2015, 2015.
  23. P. Civicioglu, E. Besdok, M. A. Gunen, and U. H. Atasever, "Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms," Neural Computing and Applications, vol. 32, no. 8, pp. 3923-3937, 2020.
  24. M. A. Gunen, E. Besdok, P. Civicioglu, and U. H. Atasever, "Camera calibration by using weighted differential evolution algorithm: a comparative study with ABC, PSO, COBIDE, DE, CS, GWO, TLBO, MVMO, FOA, LSHADE, ZHANG and BOUGUET," Neural Computing & Applications, 2020.
  25. P. Civicioglu and E. Besdok, "Bernstain-search differential evolution algorithm for numerical function optimization," Expert Systems with Applications, vol. 138, p. 112831, 2019.
  26. D. H. Choi, I. H. Jang, M. H. Kim, and N. C. Kim, "Color image enhancement using single-scale retinex based on an improved image formation model," in 2008 16th European Signal Processing Conference, 2008, pp. 1-5: IEEE.
  27. D. Ghimire and J. Lee, "Color image enhancement in HSV space using nonlinear transfer function and neighborhood dependent approach with preserving details," in 2010 Fourth Pacific-Rim Symposium on Image and Video Technology, 2010, pp. 422-426: IEEE.
  28. S. K. Naik and C. Murthy, "Hue-preserving color image enhancement without gamut problem," IEEE Transactions on image processing, vol. 12, no. 12, pp. 1591-1598, 2003.
  29. C. C. Yang and S. H. Kwok, "Efficient gamut clipping for color image processing using LHS and YIQ," Optical Engineering, vol. 42, no. 3, pp. 701-711, 2003.
  30. M. Nikolova and G. Steidl, "Fast hue and range preserving histogram specification: Theory and new algorithms for color image enhancement," IEEE transactions on image processing, vol. 23, no. 9, pp. 4087-4100, 2014.
  31. X. Su, W. Fang, Q. Shen, and X. Hao, "An image enhancement method using the quantum-behaved particle swarm optimization with an adaptive strategy," Mathematical Problems in Engineering, vol. 2013, 2013.
  32. P. Civicioglu and E. Besdok, "A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms," Artificial intelligence review, vol. 39, no. 4, pp. 315-346, 2013.
  33. P. Civicioglu and E. Besdok, "A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator," Expert Systems with Applications, vol. 103, pp. 49-62, 2018.
  34. T. Kurban, P. Civicioglu, R. Kurban, and E. Besdok, "Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding," Applied Soft Computing, vol. 23, pp. 128-143, 2014.
  35. D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied soft computing, vol. 8, no. 1, pp. 687-697, 2008.
  36. D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of global optimization, vol. 39, no. 3, pp. 459-471, 2007.
  37. D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, "A comprehensive survey: artificial bee colony (ABC) algorithm and applications," Artificial Intelligence Review, vol. 42, no. 1, pp. 21-57, 2014.
  38. D. Karaboga and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied soft computing, vol. 11, no. 1, pp. 652-657, 2011.
  39. C. Munteanu and A. Rosa, "Towards automatic image enhancement using genetic algorithms," in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), 2000, vol. 2, pp. 1535-1542: IEEE.
  40. A. Draa and A. Bouaziz, "An artificial bee colony algorithm for image contrast enhancement," Swarm and Evolutionary computation, vol. 16, pp. 69-84, 2014.
  41. J.-H. Han, S. Yang, and B.-U. Lee, "A novel 3-D color histogram equalization method with uniform 1-D gray scale histogram," IEEE Transactions on Image Processing, vol. 20, no. 2, pp. 506-512, 2010.
  42. M. Veluchamy and B. Subramani, "Image contrast and color enhancement using adaptive gamma correction and histogram equalization," Optik, vol. 183, pp. 329-337, 2019.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-04-30

Issue

Volume 7, Issue 2, March-April-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ahmet Emin Karkinli, Abdusselam Kesikoglu, " Enhancement of UAV-Aerial Images Using Weighted Differential Evolution Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 2, pp.196-206, March-April-2021. Available at doi : https://doi.org/10.32628/CSEIT217248