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Image Fusion is a process used to combine two or more images to form more informative image. More often, machine vision cameras are affected by limited depth of field and capture the clear view of the objects which are in focus. Other objects in the scene will be blurred. So, it is necessary to combine set of images to have the clear view of all objects in the scene. This is called Multi focused image fusion. This paper compares and presents the performance of second order and fourth order partial differential equation in multi focused image fusion.

Keywords

Depth of Field, Image Fusion, Multi Focused Image Fusion, Partial Differential Equations.

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References

Yong Yang, Yue Que, Shu-Ying Huang, Pan Lin, "Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network”, Signal, Image and Video Processing, 11(3), 2016, 439-446

Durga Prasad Bavirisetti and Ravindra Dhuli, "Fusion of Infrared and Visible sensor images based on Anisotropic diffusion and Karhunen Loeve Transform”, IEEE Sensors Journal, 16(1), 2015, 203-209.

Perona P, and Malik J, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., 12(7), 1990, 629–639.

You Y L, and Mostafa Kaveh. "Fourth-order partial differential equations for noise removal." IEEE Transactions on Image Processing, 9(10), 2000, 1723-1730.

Durga Prasad Bavirisetti, Gang Xiao, Gang Liu. "Multi-sensor image fusion based on fourth order partial differential equations", 20th International Conference on Information Fusion (Fusion), 2017.

S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency”, Image and Vision Computing, 26 (7), 2008, 971-979.

Xiaoye Zhang, Yong Ma, Fan Fan, Ying Zhang, and Jun Huang, "Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition," J. Opt. Soc. Am. A, 34, 2017, 1400-1410.

Wang Z., Bovik A.C., Sheik H. R. and Simoncelli E. P., “ Image Quality Assessment: From Error Visibility to Structural Similarity”, IEEE Trans. Image Processing, 13, 2004.

Haghighat, M., Aghagolzadeh, A., Seyedarabi, H., "A Non-Reference Image Fusion Metric Based on Mutual Information of Image Features," Computers and Electrical Engineering, 37(5), 2011, 744-756.

Haghighat, M., Razian, M.A., "Fast-FMI: non-reference image fusion metric," Proc. 8th International Conference on Application of Information and Communication Technologies (AICT), 2014, 1-3.

Dhirendra Pal Singh, “Role of Statistical Parameter in Digital Image Enhancement”, Int. J. Advanced Networking and Applications, 11(4), 2020, 4350-4353.

Gan W., Wu X., Wu, W., Yang, X., Ren, C., He, X., and Liu, K., “Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter,” Infrared Physics & Technology, 72, 2015, 37–51.

He K., Sun J., and Tang, X. , “Guided image ﬁltering,” in Proc. Eur. Conf. Comput. Vis., Heraklion, Greece, 2010, 1–14.

He K., Sun J., and Tang, X. , “Guided image ﬁltering,” TPAMI, 35(6), 2013,1397–1409.

Li, S., Kang, X. and Hu, J., “Image fusion with guided filtering,” Image Processing, IEEE Transactions on 22, 2013, 2864–2875.

Zhou, Z., Wang, B., Li, S. and Dong, M., “Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters,” Information Fusion, 30, 2016, 15–26.

http://www.imagenet.org/index

http://www.metapix.de/toolbox.htm

Multi focused Image Fusion using Fast Adaptive Bilateral Filter.

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Authors

K. Kannan ¹

Affiliations
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, Madurai –625701., IN

Source

International Journal of Advanced Networking and Applications, Vol 14, No 3 (2022), Pagination: 5477-5481

Abstract

This paper presents Fast Adaptive Bilateral Filter (FABF) for fusion of Multi Focuses images. Multi Focused image fusion is used to combine one or more input image into single composite image, focusing all objects in the given scene. FABF filter sharpens the image without producing under and over shoot by increasing the edge slope. This paper uses this property to decompose the input image into high and low frequency images so that different fusion rules can be used for high and low frequency images to produce good quality composite image. The performance this FABF filter in Multi focused image fusion is compared with Adaptive Bilateral Filter (ABF) using Root Mean Square Error (RMSE), Spatial Frequency (SF) and Mutual Information (MI).

Keywords

Adaptive Bilateral Filter, Fast Adaptive Bilateral Filter, Multi Focused image fusion, Root Mean Square Error, Spatial Frequency and Mutual Information.

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References

Buyue Zhang & Jan P. Allebach, “Adaptive bilateral filter for sharpness enhancement and noise removal,” IEEE Transactions on Image Processing,vol. 17, no. 5, pp. 664–678, 2008

Chaudhury, K. N., “Fast and accurate bilateral filtering using Gauss-polynomial decomposition,”Proc. IEEE International Conference on Image Processing, pp. 2005 - 2009, 2015.

Chaudhury K. N. and Rithwik, K., “Image denoising using optimally weighted bilateral filters: A SURE and fast approach,”Proc. IEEE International Conference on Image Processing, pp. 108-112, 2015.

Chaudhury, K. N. , Sage, D. and Unser, M. , “Fast O(1) bilateral filtering using trigonometric range kernels,” IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3376-3382, 2011.

Durand F., and Dorsey. J., “Fast bilateral filtering for the display of high dynamic-range images,”ACM Transactions on Graphics, vol. 21, no. 3, pp. 257-266, 2002.

Gan W., Wu X., Wu, W., Yang, X., Ren, C., He, X., and Liu, K., “Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter,”Infrared Physics & Technology 72, 37–51 (2015).

Ruturaj G. Gavaskar and Kunal N. Chaudhury, “Fast Adaptive Bilateral Filtering”, submitted to IEEE Transaction on Image Processing.

Haghighat, M., Aghagolzadeh, A., Seyedarabi, H., "A Non-Reference Image Fusion Metric Based on Mutual Information of Image Features," Computers and Electrical Engineering, vol. 37, no. 5, pp. 744-756, Sept. 2011.

Haghighat, M., Razian, M.A., "Fast-FMI: non-reference image fusion metric," 8th International Conference on Application of Information and Communication Technologies (AICT), pp. 1-3, 2014.

He K., Sun J., and Tang, X. , “Guided image filtering,”in Proc. Eur. Conf. Comput. Vis., Heraklion, Greece, Sep. 2010, pp. 1–14.

He K., Sun J., and Tang, X. , “Guided image filtering,”TPAMI, 35(6):1397–1409, 2013.

Li, S., Kang, X. and Hu, J., “Image fusion with guided filtering,”Image Processing, IEEE Transactions on 22, 2864–2875 (2013).

Li, S., Yang, B. , Hu, J. , “Performance comparison of different multi-resolution transforms for image Fusion”, Information Fusion, 12 (2), (2011), pp.74–84.

Liu, Y., Liu, S. , Wang, Z. , “A general framework for image fusion based on multi-scale transform and sparse representation”, Information Fusion, 24 (2015), pp. 147–164.

Mittal, A., R. Soundararajan, and A. C. Bovik. "Making a Completely Blind Image Quality Analyzer." IEEE Signal Processing Letters. Vol. 22, Number 3, March 2013, pp. 209–212.

Mittal, A., A. K. Moorthy, and A. C. Bovik. "No-Reference Image Quality Assessment in the Spatial Domain." IEEE Transactions on Image Processing. Vol. 21, Number 12, December 2012, pp. 4695–4708.

Paris, S., Kornprobst, P., Tumblin, J. and Durand, F. , “Bilateral Filtering: Theory and Applications”, Now Publishers Inc., 2009.

Paris S. and Durand, F. , “A fast approximation of the bilateral filter using a signal processing approach,” Proc. European Conference on Computer Vision, pp. 568-580, 2006.

Peter J. Burt and Raymond J. Kolczynski, “Enhanced Image Capture through Fusion”, Proc. IEEE International Conference, pp. 173-182,1993.

Perona P.,and Malik, J., “Scale-space and edge detection using anisotropic diffusion,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, 1990.

Porikli, F., “Constant time O(1) bilateral filtering,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.

Sugimoto K. and Kamata, S. I. , “Compressive bilateral filtering,”IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3357-3369, 2015.

Tomasi C., and Manduchi, R., “Bilateral filtering for gray and color images,”Proc. IEEE International Conference on Computer Vision, pp. 839-846, 1998.

Qu G., Zhang D., and Yan P., “Information measure for performance of image fusion,”Electron. Lett., vol. 38, no. 7, pp. 313–315,Mar. 2002.

Yang, Q. , Tan, K. H. and Ahuja, N. , “Real-time O(1) bilateral filtering,”Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 557-564, 2009.

Zhou, Z., Wang, B., Li, S. and Dong, M., “Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters,”Information Fusion 30, 15–26 (2016).

Wang ,Z., Bovik, A., Sheikh, H. and Simoncelli, E.,“Image quality assessment: From error visibility to structural similarity,”IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612,Apr. 2004.

Kesari Guru Vishnu, Kesari Eswar Bhageerath and Asrith Vatsal Pallanti, “A Comparative Analysis of Edge Detection Techniques for Processing of a Video Signal”, Int. J. Advanced Networking and Applications, 13(04), pp. 5029-5036,2022.

K. Prakash, P. Saravanamoorthi, R. Sathishkumar, M. Parimala, “A Study of Image Processing in Agriculture”, Int. J. Advanced Networking and Applications, 09(01), pp. 3311-3315, 2017.

http://www.image-net.org/index

On the Performance of Histogram Equalization Techniques in Enhancement of Proton Density Weighted Magnetic Resonance Images

Abstract Views :70 | PDF Views:1

Authors

K. Kannan ¹, S. Wesley Moses Samdoss ¹

Affiliations
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, K. Vellakulam, IN

Source

International Journal of Advanced Networking and Applications, Vol 15, No 3 (2023), Pagination: 5940– 5945

Abstract

Magnetic Resonance Images (MRI) are used by Physician to analyse the body structures to find the diseases & to monitor the treatments. For effective analysis, they should consist of all relevant information in a better visualization format. However, MRI images suffer from poor dynamic range which affects the visible quality due to low contrast. Medical Image enhancement is a powerful tool to increase the perception of information to provide better diagnosis. In this study, different histogram equalization techniques like Global Histogram Equalization (GHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), Recursive Mean Separate Histogram Equalization (RMSHE), Brightness Preserving Dynamic Histogram Equalization (BPDHE), Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE)and Contrast-limited Adaptive Histogram Equalization (CLAHE) are applied to proton density weighted magnetic resonance image to enhance the contrast and their performance is compared in terms Discrete Entropy (DE), Measure of Enhancement (EME), Average Brightness (AB) and Pixel Distance (PD). Based on the performance metrics, the best histogram equalization technique in enhancing the contrast of PD weighted MRI images is determined.

Keywords

Average Brightness, Contrast Enhancement, Discrete Entropy, Histogram Equalization, Magnetic Resonance Images, Measure of Enhancement and Pixel Distance.

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References

David Pickens, “Handbook of Medical Imaging”, Volume 1. Physics and Psychophysics, https://doi.org/10.1117/3.832716.ch6.

https://www.imaios.com/en/e-Courses/e-MRI/MRIsignal- contrast/Signal-weighting

https://radiopaedia.org/articles/mri-sequences- overview

Gonzalez R.C., Woods R.E., “Digital Image processing”, second ed. Prentice Hall 2002.

Y. T. Kim, “Contrast Enhancement Using Brightness Preserving Bi-Histogram Equation”, IEEE Transactions on Consumer Electronics, vol. 43, no. 1, (1997) February, pp. 1-8.

Y. Wang, Q. Chen and B. Zhang, “Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method”, IEEE Transactions on Consumer Electronics, vol. 45, no. 1, (1999), pp. 68-75.

S.D. Chen and A. R. Ramli, “Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation”, IEEE Transactions on Consumer Electronics, vol. 49, no. 4, (2003) November, pp. 1301-1309.

H. Ibrahim and N. S. Pik Kong, “Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement”, IEEE Transactions on Consumer Electronics, vol. 53, no. 4, (2007) November, pp. 1752- 1758.

D. Sheet, H. Garud, A. Suveer, M. Mahadevappa and J. Chatterjee, “Brightness Preserving Dynamic Fuzzy Histogram Equalization”, IEEE Transactions on Consumer Electronics, vol. 56, no. 4, (2010) November, pp. 2475-2480.

Zuiderveld, Karel, “Contrast Limited Adaptive Histograph Equalization.” Graphic Gems IV. San Diego: Academic Press Professional, 474–485, 1994.

C. E. Shannon, "A mathematical theory of communication," Bell Syst. Tech. J., vol. 27, pp. 623- 656, Oct. 1948.

S. Again, B. Silver, and K. Panetta, "Transform coefficient histogram-based image enhancement algorithms using contrast entropy," IEEE Trans. Image Process., vol. 16, no. 3, pp. 741-758, Mar. 2007.

Z. Chen, B. R. Abidi, D. L. Page, and M. A. Abidi, "Gray-level grouping (GLG): An automatic method for optimized image contrast enhanceemnt-part I: The basic method," IEEE Trans. Image Process., vol. 15, no. 8, pp. 2290-2302, Aug. 2006.

https://data.lhncbc.nlm.nih.gov

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