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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background:

Empirical curvelet and ridgelet image fusion is an emerging technique in the field of image processing that aims to combine the benefits of both transforms.

Objective:

The proposed method begins by decomposing the input images into curvelet and ridgelet coefficients using respective transform algorithms for Computerized Tomography (CT) and magnetic Resonance Imaging (MR) brain images.

Methods:

An empirical coefficient selection strategy is then employed to identify the most significant coefficients from both domains based on their magnitude and directionality. These selected coefficients are coalesced using a fusion rule to generate a fused coefficient map. To reconstruct the image, an inverse curvelet and ridgelet transform was applied to the fused coefficient map, resulting in a high-resolution fused image that incorporates the salient features from both input images.

Results:

The experimental outcomes on real-world datasets show how the suggested strategy preserves crucial information, improves image quality, and outperforms more conventional fusion techniques. For CT Ridgelet-MR Curvelet and CT Curvelet-MR Ridgelet, the authors' maximum PSNRs were 58.97 dB and 55.03 dB, respectively. Other datasets are compared with the suggested methodology.

Conclusion:

The proposed method's ability to capture fine details, handle complex geometries, and provide an improved trade-off between spatial and spectral information makes it a valuable tool for image fusion tasks.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-07-11
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References

  1. PalB JainS Detection of cerebrovascular diseases using novel discrete component wavelet cosine transform.Current Computer-Aided Drug Design2023192137149
    [Google Scholar]
  2. JainS. SalauA.O. Multimodal Image Fusion Employing Discrete Cosine Transform, 2021 IEEE International Women in Engineering (WIE)Conference on Electrical and Computer Engineering (WIECON-ECE)Dhaka BangladeshDec 3-5, 202158
    [Google Scholar]
  3. PalB JainS Novel discrete component wavelet transform for detection of cerebrovascular diseases.Sadhana2022474237
    [Google Scholar]
  4. JainS. SachdevaM. DubeyP. VijanA. Multi-sensor Image Fusion Using Intensity Hue Saturation Technique. Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science. LuhachA. JatD. HawariK. GaoX.Z. LingrasP. SingaporeSpringer2019107614715710.1007/978‑981‑15‑0111‑1_14
    [Google Scholar]
  5. VijanA. DubeyP. JainS. Comparative analysis of various image fusion techniques for brain magnetic resonance images.Procedia Comput. Sci.202016741342210.1016/j.procs.2020.03.250
    [Google Scholar]
  6. SalauA.O. JainS. EnehJ.N. A review of various image fusion types and transforms.Indonesian Journal of Electrical Engineering and Computer Science20212431515152210.11591/ijeecs.v24.i3.pp1515‑1522
    [Google Scholar]
  7. EladM. AharonM. Image denoising via sparse and redundant representations over learned dictionaries.IEEE Trans. Image Process.200615123736374510.1109/TIP.2006.88196917153947
    [Google Scholar]
  8. WangZ. BovikA.C. SheikhH.R. SimoncelliE.P. Image quality assessment: From error visibility to structural similarity.IEEE Transactions on Image Processing200413460061210.1109/TIP.2003.81986115376593
    [Google Scholar]
  9. XieH. MoulinP. Fast and robust multiframe super resolution.IEEE Trans. Image Process.2002104579593
    [Google Scholar]
  10. RahmaniA.I. AlmasiM. SalehN. KatouliM. Image fusion of noisy images based on simultaneous empirical wavelet transform.IIETA2020375703710
    [Google Scholar]
  11. Shreyamsha KumarB.K. Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform.Signal Image Video Process.2013761125114310.1007/s11760‑012‑0361‑x
    [Google Scholar]
  12. KarimpourA. RahmatallaS. Extended Empirical wavelet transformation: Application to structural updating.J. Sound Vibrat.202150011602610.1016/j.jsv.2021.116026
    [Google Scholar]
  13. HuratB AlvaradoZ GillesJ The empirical watershed wavelet.Journal of Imaging2020612140
    [Google Scholar]
  14. FeiS.W. The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction.Int. J. Green Energy2020171058359010.1080/15435075.2020.1779076
    [Google Scholar]
  15. HaiRunH. An improved empirical wavelet transform method for rolling bearing fault diagnosis.Science China Technological Sciences2021631122312240
    [Google Scholar]
  16. NasrM.E. An integrated image fusion technique for boosting the quality of noisy remote sensing images.National Radio Science Conference200710.1109/NRSC.2007.371370
    [Google Scholar]
  17. KumarT.S. HussainM.A. KanhangadV. Classification of voiced and non-voiced speech signals using empirical wavelet transform and multi-level local patterns.IEEE International Conference on Digital Signal Processing (DSP).201510.1109/ICDSP.2015.7251851
    [Google Scholar]
  18. AminiN. FatemizadehE. BehnamH. MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules.J. Med. Eng. Technol.201438421121910.3109/03091902.2014.90401424758393
    [Google Scholar]
  19. RattanapitakW. UdomhunsakulS. Comparative efficiency of color models for multi-focus color image fusion.Hong Kong2010
    [Google Scholar]
  20. LuH. ZhangL. SerikawaS. Maximum local energy: An effective approach for multisensor image fusion in beyond wavelet transform domain.Comput. Math. Appl.2012645996100310.1016/j.camwa.2012.03.017
    [Google Scholar]
  21. HossnyM. NahavandiS. CreightonD. Comments on ‘Information measure for performance of image fusion’.Electron. Lett.200844181066106710.1049/el:20081754
    [Google Scholar]
  22. SheikhH.R. BovikA.C. Image information and visual quality.2004 IEEE International Conference on Acoustics, Speech, and Signal Processing3iii-7092004
    [Google Scholar]
  23. QuG. ZhangD. YanP. Medical image fusion by wavelet transforms modulus maxima.Optics Express201494184190
    [Google Scholar]
  24. AriciT. DikbasS. AltunbasakY. A histogram modification framework and its application for image fusion.IEEE Trans. Image Process.20091891921193510.1109/TIP.2009.202154819403363
    [Google Scholar]
  25. Shutao Li Xudong Kang Jianwen Hu Image fusion with guided filtering.IEEE Trans. Image Process.20132272864287510.1109/TIP.2013.224422223372084
    [Google Scholar]
  26. MaJ. SunW. ZhangM. HeZ. Infrared and visible image fusion with convolutional neural networks.IEEE Trans. Instrum. Meas.201969521362147
    [Google Scholar]
  27. PetrovicV. DelacC. Image fusion: A survey of the state of the art.Inf. Fusion2011121217
    [Google Scholar]
  28. Available from:https://www.kaggle.com/datasets/darren2020/ct-to-mri-cgan
  29. JamwalA. JainS. Classification of multimodal Brain Images employing a novel Ridgempirical Transform.Neuroquantology202220628712882
    [Google Scholar]
  30. JamwalA. JainS. Robust multimodal fusion network employing novel Empirical Riglit Wavelet Transform for brain images, Measurement.Sensors202224December100529
    [Google Scholar]
  31. JainS. Classification of Protein Kinase B using discrete wavelet transform.International Journal of Information Technology201810221121610.1007/s41870‑018‑0090‑7
    [Google Scholar]
  32. GillesJ. TranG. OsherS. 2D empirical transforms. wavelets, ridgelets, and curvelets revisited.SIAM J. Imaging Sci.20147115718610.1137/130923774
    [Google Scholar]
  33. PalB. MahajanS. JainS. A comparative study of traditional image fusion techniques with a novel hybrid method.2020 International Conference on Computational Performance Evaluation (ComPE)202010.1109/ComPE49325.2020.9200017
    [Google Scholar]
  34. Atlas the whole brain. Available from:https://www.med.harvard.edu/aanlib/
  35. SinghR. KhareA. Multiscale medical image fusion in wavelet domain.ScientificWorldJournal2013201311010.1155/2013/52103424453868
    [Google Scholar]
  36. OliveiraF.P.M. TavaresJ.M.R.S. Medical image registration: A review.Comput. Methods Biomech. Biomed. Engin.2014172739310.1080/10255842.2012.67085522435355
    [Google Scholar]
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