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Image Fusion in Discrete Cosine Transform Domain using Masking Techniques

Image Fusion in Discrete Cosine Transform Domain using Masking Techniques. CONTENTS. Image fusion Fusion techniques Literature survey Proposed techniques Mask Rectangular mask Triangular mask Fan shaped mask Strip mask Image fusion in transform domain using masking

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Image Fusion in Discrete Cosine Transform Domain using Masking Techniques

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  1. Image Fusion in Discrete Cosine Transform Domain using MaskingTechniques

  2. CONTENTS • Image fusion • Fusion techniques • Literature survey • Proposed techniques • Mask • Rectangular mask • Triangular mask • Fan shaped mask • Strip mask • Image fusion in transform domain using masking • Performance evaluation of image fusion techniques. • Signal to noise ratio error • Root mean square • Result of existing technique • Comparison of image fusion using different mask. • Conclusion • References

  3. What is “IMAGE FUSION”……….?? • Image fusion combines multiple images of the same scene into a single image which is suitable for human perception and practical applications. • Image fusion is applicable for numerous fields including: defence systems, remote sensing and geosciences, robotics and industrial engineering, and medical imaging.

  4. Fusion Techniques • The most important issue concerning image fusion is to determine how to combine the sensor images. • Fusion techniques are commonly divided into two categories: • Spatial DomainTechniques: • Transform DomainTechniques :

  5. Literature survey Many fusion rules have been proposed in the existing literature, which are categorized, as follows: • Fuse by averaging the corresponding coefficients in each image (‘mean’ rule). • Fuse by selecting the greatest in absolute value of the corresponding coefficients in each image (‘max-abs’ rule).

  6. In existing literature Several transforms have already been used such as DCT, DST, DFT, DWT in fusion application. The steps of algorithm based on transform domain technique are summarised as follow : (i) Given images, take the transform of these images. (ii) Obtain the transform coefficients of the images. (iii) Fuse the images by proper selection rule. (iv)Take the inverse transform. (v) Obtain the fused image.

  7. Proposed technique: • This paper investigates the effect of use of different types of masks in discrete cosine transform (DCT) domain for image fusion applications. • Here we have used different types of masks such as rectangular, triangular, strip and fan shaped mask.

  8. Mask • Masking is used to retain some portion of one image and some of other image. • Here, I have studied four type of mask, which are given below........ • Rectangular mask • Triangular mask • Fan shaped mask • Strip mask

  9. Rectangular Mask

  10. Triangular Mask

  11. Fanshape Mask

  12. Strip Mask

  13. Image fusion in transform domain using masking MASKING OF IMAGE T(F1) ADDING MASKED IMAGE TRANSFORM OF IMAGE F1 IMAGE F1 COMPLEME-NTARY MASKING OF IMAGE T(F2) TRANSFORM OF IMAGE F2 IMAGE F2 INVERSE OF ADDED IMAGE FUSED IMAGE

  14. The steps of image fusion algorithm in DCT domain by masking technique are summarised as follow : • Given two images, F1 and F2. • Taking transform(DCT) of the images F1 and F2. • Apply mask on both transformed images and added. • Taking inverse transform of resultant image. • Resultant image is the FUSED image.

  15. Image F1 Image F2 Test Images which are used in this paper for image fusion

  16. Image Fusion using Rectangular Mask

  17. Image Fusion using Triangular Mask

  18. Image Fusion using Strip Mask

  19. Image Fusion using Fanshaped Mask

  20. Performance evaluation of image fusion techniques • The acceptable quality for the fused image is set by the receiver of the image which is usually the human observer. • Therefore, quality assessments of fused images are traditionally carried out by visual analysis. • Other than human visual analysis ,we introduce some statistical measures such as the SNR, PSNR, and MSE, RMSE which require an ideal or reference image when applied.

  21. Signal-to-Noise-Ratio (SNR) • The quality of a signal is often expressed quantitatively with the signal-to-noise ratio defined as: • Where Energy signal is the sum of the squares of the signal values. • Energy noise is the sum of the squares of the noise samples. • where z(m,n) is our estimated signal(fused image) and o(m,n) is original signal.

  22. Root Mean Square Error • The RMSE between the reference image R and the fused image F is: • where R(i, j)and F(i,j)are the pixel values at the (i, j )coordinates of the reference image and the fused image, respectively. • The image size is I ×J.

  23. Result of existing technique using different transforms

  24. Result of existing technique using different transforms

  25. Comparision of fused image using different masks Out of focus image 1 Out of focus image 2 Reference image Fan shaped mask Rectangular mask Triangular mask Strip mask

  26. Comparision of various masking scheme

  27. Conclusion • In this paper, a new method for image fusion has been proposed using masking technique in DCT domain. • The image fusion results of proposed method have been compared with some existing technique. • The results show that this method can achieve better quality of fused image using fan shape mask as compared to other mask.

  28. REFERENCE • [1] Daniel Mueller1, Anthony maeder2, and Peter o’shea1, “The generalised image fusion toolkit (gift),” Release 4.0, September 13, (2006) 1queensland university of technology, Brisbane, australia,2e-health research centre,csiro ict centre, Brisbane, Australia. • [2] Mohamed, M.A. “Implementation of image fusion techniques for multi-focus images using FPGA” Radio science conference (NRSC). • [3] Anil K. Jain, [Fundamentals of digital image processing],PHI. • [4] Gema Piella Fenoy, “Adaptive wavelets and their applications to image fusion and compression,” Thesis of the research work that has been carried out at the centre for mathematics and computer science (CWI), Amsterdam,(2003). • [5] Tania Stathaki , [Image fusion], Academic press, (2008) • [6] H.B. Mitchell , “Image fusion”. Springer-verlag , Berlin, (2008). • [7] K. K. Sharma and Mohit Sharma, “Image fusion based on image decomposition using self-fractional Fourier functions”, Signal, image and video processing (2012), doi: 10.1007/s11760-012-0363-8.

  29. [8] Nikolas Mitianoudis , Tania Stathaki, “Pixel-based and region-based image fusion schemes using ice bases,”Communications and signal processing group, imperial college London, exhibition road, sw7 2az London, uk. • [9] Syed Ali Khayam, “The discrete cosine transform(DCT) theory and application1,” Department of electrical & computer engineering michigan state university march 10th (2003) • [10] B.Chanda and D.Dutta Majumdar , [Digital image processing and analysis], Prentice hall of India, 2000. • [11] Chipman, l. J., and Orr, T. M. “Wavelets and image fusion,” In proceedings of the IEEE international conference on image processing (Washington D.C., October 24-26 ,(1995)), pp. 248-251. • [12] Deepa Li a. Godse, Dattatraya S. Bormane, “Wavelet based image fusion using pixel based maximum selection rule,” Deepali a. Godse et al. / international journal of engineering science and technology (IJEST). • [13] Haritha Mallavarpu, Iman Tahvili, Hemanth Verramestti and Wlodek J. Kulesz, “Implementation of image fusion algorithm using discrete cosine transform (DCT-iii),” s15_jasmineblommor. • [14] S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on pattern analysis and machine intelligence 11 (1989), 674-693.

  30. [15] S. G. Mallat, [A wavelet tour of signal processing], Academic press, San Diego, (1998). • [16] G.H. Qu, D. Zhang and P. Yan, “Information measure for performance of image fusion,” Electronics letters,vol. 38, no. 7, (2002), pp. 313–315. • [17] C.S. Xydeas and V. Petrovic, “Objective image fusion performance measure,” Electronics letters, vol. 36, no.4, (2000), pp. 308–309. • [18] Q. Wang, Y. Shen, Y. Zhang and J.Q. Zhang, “A quantitative method for evaluating the performances of hyper spectral image fusion,” IEEE Transactions on instrumentation and measurement, vol. 52, no. 4, (2003), pp.1041–1047. • [19] Q. Wang, Y. Shen, y. Zhang and J.Q. Zhang, “Performance evaluation of image fusion techniques,” • Department of control science & engineering, Harbin institute of technology, T.R china. • [20] Arash Golibagh Mahyari and Mehran Yazdi, member, IEEE, “Panchromatic and multispectral image fusion based on maximization of both spectral and spatial similarities,” IEEE Transactions on geosciences and remote • sensing, vol. 49, no. 6, June (2011).

  31. THANK YOU

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