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Kannan, K.
- Application of Partial Differential Equations in Multi Focused Image Fusion
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Authors
Affiliations
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, Madurai – 625 701, IN
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, Madurai – 625 701, IN
Source
International Journal of Advanced Networking and Applications, Vol 14, No 1 (2022), Pagination: 5266-5270Abstract
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.References
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- 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.
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- http://www.imagenet.org/index
- http://www.metapix.de/toolbox.htm
- Multi focused Image Fusion using Fast Adaptive Bilateral Filter.
Abstract Views :94 |
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Authors
Affiliations
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, Madurai –625701., IN
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: 5477-5481Abstract
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.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
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- 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.
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- 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).
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- http://www.image-net.org/index
- On the Performance of Histogram Equalization Techniques in Enhancement of Proton Density Weighted Magnetic Resonance Images
Abstract Views :78 |
PDF Views:1
Authors
Affiliations
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, K. Vellakulam, IN
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– 5945Abstract
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.References
- David Pickens, “Handbook of Medical Imaging”, Volume 1. Physics and Psychophysics, https://doi.org/10.1117/3.832716.ch6.
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