Frequency upon Image Enhancement by using Transform

Authors

  • Dr. N. Geetha Rani  Associate Professor, Department of ECE, Ravindra college of Engineering for Women, Kurnool, Andhra Pradesh, India
  • Y. Chandu Priya  Department of ECE, Ravindra college of Engineering for Women, Kurnool, Andhra Pradesh, India
  • T. Moksha Sai Devi  Department of ECE, Ravindra college of Engineering for Women, Kurnool, Andhra Pradesh, India
  • S. Aneesa Fathima  Department of ECE, Ravindra college of Engineering for Women, Kurnool, Andhra Pradesh, India
  • S. Mounika  Department of ECE, Ravindra college of Engineering for Women, Kurnool, Andhra Pradesh, India

Keywords:

Image Fusion, Multi-Focus, Visual Sensor Networks, Discrete Wavelet Transform, Discrete Cosine Transform.

Abstract

The purpose of multi-focus image fusion is to gather the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using the focus measurement in the spatial domain. However, multi-focus image fusion processing is very time-saving and appropriate in discrete cosine transform (DCT) domain, especially when JPEG images are used in visual sensor networks. Thus most of the researchers are interested in focus measurement calculations and fusion processes directly in the DCT domain. Accordingly, many researchers have developed some techniques that substitute the spatial domain fusion process with the DCT domain fusion process. Previous works on the DCT domain have some shortcomings in the selection of suitable divided blocks according to their criterion for focus measurement. In this paper, calculation of two powerful focus measurements, proposed directly in the DCT domain.

References

  1. Drajic, D. & Cvejic, N. (2007). Adaptive fusion of multimodal surveillance image sequences in visual sensor networks. IEEE Transactions on Consumer Electronics, vol. 53, no. 4, pp. 1456-1462.
  2. Wu, W., Yang, X., Pang, Y., Peng, J. & Jeon, G. (2013). A multifocus image fusion method by using hidden Markov model. Optics Communication, vol. 287, January, pp. 63-72.
  3. Kumar, B., Swamy, M. & Ahmad, M. O. (2013). Multiresolution DCT decomposition for multifocus image fusion. In 26th Annual IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1-4.
  4. Haghighat, M. B. A, Aghagolzadeh, A. & Seyedarabi, H. (2010). Real-time fusion of multi-focus images for visual sensor networks. In 6th Iranian Machine Vision and Image Processing (MVIP), pp. 1- 6.
  5. Naji, M. A., & Aghagolzadeh, A. (2015). A new multi-focus image fusion technique based on variance in DCT domain. In 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 478-484.
  6. Castanedo, F., García, J., Patricio, M., & Molina, J.M. (2008). Analysis of distributed fusion alternatives in coordinated vision agents. In 11th International Conference on Information Fusion, pp. 1-6.
  7. Kazemi, V., Seyedarabi, H. & Aghagolzadeh, A. (2014). Multifocus image fusion based on compressive sensing for visual sensor networks. In 22nd Iranian Conference on Electrical Engineering (ICEE), pp. 1668-1672.
  8. Soro, S., & Heinzelman, W. (2009). A Survey of Visual Sensor Networks. Advances in Multimedia. vol. 2009, Article ID 640386, 21 pages, May.
  9. Huang, W. & Jing, Z. (2007). Evaluation of focus measures in multi-focus image fusion. Pattern Recognition Letters, vol. 28, no. 4, pp. 493-500.
  10. Li, S., Kwok, J. T., & Wang, Y. (2001). Combination of images with diverse focuses using the spatial frequency, Information fusion, vol. 2, no. 3, pp. 169-176.
  11. Li, S., & Yang, B. (2008). Multifocus Image Fusion Using Region Segmentation and Spatial Frequency. Image and Vision Computing, vol. 26, no. 7, pp. 971-979.
  12. Hongmei, W., Cong, N., Yanjun, L., & Lihua, C. (2011). A Novel Fusion Algorithm for Multi-focus Image. International Conference on Applied Informatics and Communication (ICAIC), pp. 641-647.
  13. Mahajan, S., & Singh, A. (2014). A Comparative Analysis of Different Image Fusion Techniques. IPASJ International Journal of Computer Science (IIJCS), vol. 2, no. 1, pp. 634-642.
  14. Pertuz, S., Puig, S. D., & Garcia, M. A. (2013). Analysis of focus measure operators for shape-from- focus. Pattern Recognition. Vol. 46, no. 5, pp.1415- 1432.
  15. Kaur, P., & Kaur, M. (2015). A Comparative Study of Various Digital Image Fusion Techniques: A Review. International Journal of Computer Applications, vol. 114, no. 4.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

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

How to Cite

[1]
Dr. N. Geetha Rani, Y. Chandu Priya, T. Moksha Sai Devi, S. Aneesa Fathima, S. Mounika, " Frequency upon Image Enhancement by using Transform, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.358-367, March-April-2022.