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Current Trends in Science and Technology

an Open Access Publication ISSN: 0976-9730 | 0976-9498

Engineering and Technology

A New Image Enhancement Technique using Local-Correlation based Fusion using Wavelet Transform

Shashi Kant
Prabhishek Singh
Research Scholar, Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow,India
Madan Kushwaha
Assistant Professor, Computer Science & Engineering, Bansal Institute of Engineering & Technology, Lucknow, India
Online First: May 03, 2018
| Google Scholar

Abstract

Fusion of images is the process of extracting important features from two or more images of the same scene into a single image for the better visual perception and computer processing tasks such as segmentation, feature extraction and object recognition. Discrete wavelet transform (DWT) allows the image decomposition for preserving the discrete image information. DWT is applied to analyze the images efficiently in detail. This paper proposed a new image enhancement scheme using wavelet transform based on local-correlation based fusion strategy. The proposed method is compared with the standard DWT and Stationary Wavelet Transform (SWT) method of fusion using max and avg rule. The experimental results are evaluated and compared using visual appearance and quantitative measure like Peak Signal to Noise Ratio and Correlation Coefficient. The proposed work is best in removing the blurred regions and maintains the smoothness in the homogeneous part of the image.

  Submitted
May 3, 2018
Published
May 3, 2018
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References

1. Wang, Wencheng and Chang, Faliang, “A multi-focus image fusion method based on Laplacian pyramid”, Journal of Computers, 2011. 2. Li, Shutao and Kwok, JT-Y and Tsang, Ivor W and Wang, Yaonan, “Fusing images with different focuses using support vector machines”, Neural Networks, IEEE Transactions on. IEEE, 2004, pp. 1555–1561. 3. Liang, Junli and He, Yang and Liu, Ding and Zeng, Xianju, “Image fusion using higher order singular value decomposition”, Image Processing, IEEE Transactions on, vol. 31, no. 12, pp. 2898–2909, 2012 2013. 4. Li, Shutao and Kang, Xudong and Hu, Jianwen, “Image fusion with guided filtering”, IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, IEEE, vol. 27, no. 2, pp.2864–2875, 2013. 5. Singh, P., Shree, R. A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. Journal of King Saud University – Computer and Information Sciences (2017). 6. Prabhishek Singh, Raj Shree, “A New Computationally Improved Homomorphic Despeckling Technique of SAR Images”, Volume 8, No. 3, March – April 2017, International Journal of Advanced Research in Computer Science. 7. Prabhishek Singh, Raj Shree, “Statistical Modelling of Log Transformed Speckled Image”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 8, August 2016. 8. Shutao Li, Bin Yang, “Multifocus image fusion using region segmentation and spatial frequency”, Image and Vision Computing, Volume 26, Issue 7, 2 July 2008, Pages 971-979. 9. Burt, P.J., Kolczynski, R.J., 1993. Enhanced image capture through fusion. In: Proc. 4th Internat. Conf. on Computer Vision, Berlin, Germany, pp. 173–182. 10. Burt, P.T., Andelson, E.H., 1983. The Laplacian pyramid as a compact image code. IEEE Trans. Comm. 31, 532–540. 11. Eskicioglu, A.M., Fisher, P.S., 1995. Image quality measures and their performance. IEEE Trans. Comm. 43 (12), 2959–2965. 12. Li, H., Manjunath, B.S., Mitra, S.K., 1995. Multisensor image fusion using the wavelet transform. Graphical Models Image Processing 57 (3), 235–245. 13. Yocky, D.A., 1995. Image merging and data fusion by means of the discrete two-dimensional wavelet transform. J. Opt. Soc. Am. A: Opt., Image Sci. Vision 12 (9), 1834– 1841. 14. G. Pajares and J. Manuel de la Cruz, "A wavelet-based image fusion tutorial," Pattern Recognition, vol. 37, pp. 1855-1872, September 2004. 15. K. Rani and R. Sharma, “Study of image fusion using discrete wavelet and multiwavelet transform”. 16. V. Naidu and J. Raol, “Pixel-level image fusion using wavelets and principal component analysis”, Defence Science Journal, vol. 58, no. 3, pp. 338–352, 2008. 17. H. Li, B.S. Manjunath, and S.K. Mitra. Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57:235–245, 1995. 18. Nejati, Mansour. (2016). Lytro Multi-focus Image Dataset. 10.13140/RG.2.1.4802.6647. 19. Prabhishek Singh, Raj Shree, Speckle Noise: Modelling and Implementation, published in International Journal of Control Theory and Applications, 9(17), pp. 8717-8727 © International Science Press. (2016) 20. Prabhishek Singh, Raj Shree, Statistical Quality Analysis of Wavelet Based SAR Images in Despeckling Process, Asian Journal of Electrical Sciences (AJES), Volume 6 No.2 July-December pp 1-18. (2017) 21. Prabhishek Singh, Raj Shree, Quantitative Dual Nature Analysis of Mean Square Error in SAR Image Despeckling, in International Journal on Computer Science and Engineering (IJCSE), Volume 9 Number 11 Nov, Page: 619-622. (2017) 22. Prabhishek Singh, Raj Shree, Analysis and Effects of Speckle Noise in SAR Images, 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), Pages: 1 – 5. (IEEE International Conference). (2016) 23. Manoj Diwakar, Manoj Kumar, “A review on CT image noise and its denoising”, Biomedical Signal Processing and Control, Volume 42, April 2018, Pages 73-88 24. ManojDiwakar, ManojKumar, “CT image denoising using NLM and correlation-based wavelet packet thresholding”, IET Image Processing, 2018, 0 pp. DOI: 10.1049/iet-ipr.2017.0639. 25. Prabhishek Singh, Raj Shree, “A Comparative Study to Noise Models and ImageRestoration Techniques”, International Journal of Computer Applications (0975 – 8887)Volume 149 – No.1, September 2016. 26. Prabhishek Singh, Raj Shree, Manoj Diwakar, “A new SAR image despeckling using correlation based fusion and method noise thresholding”, Journal of King Saud University – Computer and Information Sciences (2018), Elsevier, https://doi.org/10.1016/j.jksuci.2018.03.009. 27. Prabhishek Singh, Raj Shree, “Importance Of DWT In Despeckling SAR Images And Experimentally Analyzing The Wavelet Based Thresholding Techniques”, in International Journal Of Engineering Sciences & Research Technology, 5(10): October, 2016
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References

1. Wang, Wencheng and Chang, Faliang, “A multi-focus image fusion method based on Laplacian pyramid”, Journal of Computers, 2011.
2. Li, Shutao and Kwok, JT-Y and Tsang, Ivor W and Wang, Yaonan, “Fusing images with different focuses using support vector machines”, Neural Networks, IEEE Transactions on. IEEE, 2004, pp. 1555–1561.
3. Liang, Junli and He, Yang and Liu, Ding and Zeng, Xianju, “Image fusion using higher order singular value decomposition”, Image Processing, IEEE Transactions on, vol. 31, no. 12, pp. 2898–2909, 2012 2013.
4. Li, Shutao and Kang, Xudong and Hu, Jianwen, “Image fusion with guided filtering”, IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, IEEE, vol. 27, no. 2, pp.2864–2875, 2013.
5. Singh, P., Shree, R. A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. Journal of King Saud University – Computer and Information Sciences (2017).
6. Prabhishek Singh, Raj Shree, “A New Computationally Improved Homomorphic Despeckling Technique of SAR Images”, Volume 8, No. 3, March – April 2017, International Journal of Advanced Research in Computer Science.
7. Prabhishek Singh, Raj Shree, “Statistical Modelling of Log Transformed Speckled Image”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 8, August 2016.
8. Shutao Li, Bin Yang, “Multifocus image fusion using region segmentation and spatial frequency”, Image and Vision Computing, Volume 26, Issue 7, 2 July 2008, Pages 971-979.
9. Burt, P.J., Kolczynski, R.J., 1993. Enhanced image capture through fusion. In: Proc. 4th Internat. Conf. on Computer Vision, Berlin, Germany, pp. 173–182.
10. Burt, P.T., Andelson, E.H., 1983. The Laplacian pyramid as a compact image code. IEEE Trans. Comm. 31, 532–540.
11. Eskicioglu, A.M., Fisher, P.S., 1995. Image quality measures and their performance. IEEE Trans. Comm. 43 (12), 2959–2965.
12. Li, H., Manjunath, B.S., Mitra, S.K., 1995. Multisensor image fusion using the wavelet transform. Graphical Models Image Processing 57 (3), 235–245.
13. Yocky, D.A., 1995. Image merging and data fusion by means of the discrete two-dimensional wavelet transform. J. Opt. Soc. Am. A: Opt., Image Sci. Vision 12 (9), 1834– 1841.
14. G. Pajares and J. Manuel de la Cruz, "A wavelet-based image fusion tutorial," Pattern Recognition, vol. 37, pp. 1855-1872, September 2004.
15. K. Rani and R. Sharma, “Study of image fusion using discrete wavelet and multiwavelet transform”.
16. V. Naidu and J. Raol, “Pixel-level image fusion using wavelets and principal component analysis”, Defence Science Journal, vol. 58, no. 3, pp. 338–352, 2008.
17. H. Li, B.S. Manjunath, and S.K. Mitra. Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57:235–245, 1995.
18. Nejati, Mansour. (2016). Lytro Multi-focus Image Dataset. 10.13140/RG.2.1.4802.6647.
19. Prabhishek Singh, Raj Shree, Speckle Noise: Modelling and Implementation, published in International Journal of Control Theory and Applications, 9(17), pp. 8717-8727 © International Science Press. (2016)
20. Prabhishek Singh, Raj Shree, Statistical Quality Analysis of Wavelet Based SAR Images in Despeckling Process, Asian Journal of Electrical Sciences (AJES), Volume 6 No.2 July-December pp 1-18. (2017)
21. Prabhishek Singh, Raj Shree, Quantitative Dual Nature Analysis of Mean Square Error in SAR Image Despeckling, in International Journal on Computer Science and Engineering (IJCSE), Volume 9 Number 11 Nov, Page: 619-622. (2017)
22. Prabhishek Singh, Raj Shree, Analysis and Effects of Speckle Noise in SAR Images, 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), Pages: 1 – 5. (IEEE International Conference). (2016)
23. Manoj Diwakar, Manoj Kumar, “A review on CT image noise and its denoising”, Biomedical Signal Processing and Control, Volume 42, April 2018, Pages 73-88
24. ManojDiwakar, ManojKumar, “CT image denoising using NLM and correlation-based wavelet packet thresholding”, IET Image Processing, 2018, 0 pp. DOI: 10.1049/iet-ipr.2017.0639.
25. Prabhishek Singh, Raj Shree, “A Comparative Study to Noise Models and ImageRestoration Techniques”, International Journal of Computer Applications (0975 – 8887)Volume 149 – No.1, September 2016.
26. Prabhishek Singh, Raj Shree, Manoj Diwakar, “A new SAR image despeckling using correlation based fusion and method noise thresholding”, Journal of King Saud University – Computer and Information Sciences (2018), Elsevier, https://doi.org/10.1016/j.jksuci.2018.03.009.
27. Prabhishek Singh, Raj Shree, “Importance Of DWT In Despeckling SAR Images And Experimentally Analyzing The Wavelet Based Thresholding Techniques”, in International Journal Of Engineering Sciences & Research Technology, 5(10): October, 2016
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