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Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images

In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely the preprocessing means sharpening performed o...

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Autores principales: Khan, Sarwar Shah, Khan, Muzammil, Alharbi, Yasser, Haider, Usman, Ullah, Kifayat, Haider, Shahab
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684527/
https://www.ncbi.nlm.nih.gov/pubmed/34931139
http://dx.doi.org/10.1155/2021/7000991
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author Khan, Sarwar Shah
Khan, Muzammil
Alharbi, Yasser
Haider, Usman
Ullah, Kifayat
Haider, Shahab
author_facet Khan, Sarwar Shah
Khan, Muzammil
Alharbi, Yasser
Haider, Usman
Ullah, Kifayat
Haider, Shahab
author_sort Khan, Sarwar Shah
collection PubMed
description In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely the preprocessing means sharpening performed on the images before applying fusion techniques. In this paper, along with the novel concept, a new sharpening technique, Laplacian filter + discrete Fourier transform (LF + DFT), is also proposed. The LF is used to recognize the meaningful discontinuities in an image. DFT recognizes that the rapid change in the image is like sudden changes in the frequencies, low-frequency to high-frequency in the images. The aim of image sharpening is to highlight the key features, identifying the minor details, and sharpen the edges while the previous methods are not so effective. To validate the effectiveness the proposed method, the fusion is performed by a couple of advanced techniques such as stationary wavelet transform (SWT) and discrete wavelet transform (DWT) with both types of images like grayscale and color image. The experiments are performed on nonmedical and medical (breast medical CT and MRI images) datasets. The experimental results demonstrate that the proposed method outperforms all evaluated qualitative and quantitative metrics. Quantitative assessment is performed by eight well-known metrics, and every metric described its own feature by which it is easily assumed that the proposed method is superior. The experimental results of the proposed technique SWT (LF + DFT) are summarized for evaluation matrices such as RMSE (5.6761), PFE (3.4378), MAE (0.4010), entropy (9.0121), SNR (26.8609), PSNR (40.1349), CC (0.9978), and ERGAS (2.2589) using clock dataset.
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spelling pubmed-86845272021-12-19 Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images Khan, Sarwar Shah Khan, Muzammil Alharbi, Yasser Haider, Usman Ullah, Kifayat Haider, Shahab J Healthc Eng Research Article In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely the preprocessing means sharpening performed on the images before applying fusion techniques. In this paper, along with the novel concept, a new sharpening technique, Laplacian filter + discrete Fourier transform (LF + DFT), is also proposed. The LF is used to recognize the meaningful discontinuities in an image. DFT recognizes that the rapid change in the image is like sudden changes in the frequencies, low-frequency to high-frequency in the images. The aim of image sharpening is to highlight the key features, identifying the minor details, and sharpen the edges while the previous methods are not so effective. To validate the effectiveness the proposed method, the fusion is performed by a couple of advanced techniques such as stationary wavelet transform (SWT) and discrete wavelet transform (DWT) with both types of images like grayscale and color image. The experiments are performed on nonmedical and medical (breast medical CT and MRI images) datasets. The experimental results demonstrate that the proposed method outperforms all evaluated qualitative and quantitative metrics. Quantitative assessment is performed by eight well-known metrics, and every metric described its own feature by which it is easily assumed that the proposed method is superior. The experimental results of the proposed technique SWT (LF + DFT) are summarized for evaluation matrices such as RMSE (5.6761), PFE (3.4378), MAE (0.4010), entropy (9.0121), SNR (26.8609), PSNR (40.1349), CC (0.9978), and ERGAS (2.2589) using clock dataset. Hindawi 2021-12-11 /pmc/articles/PMC8684527/ /pubmed/34931139 http://dx.doi.org/10.1155/2021/7000991 Text en Copyright © 2021 Sarwar Shah Khan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Khan, Sarwar Shah
Khan, Muzammil
Alharbi, Yasser
Haider, Usman
Ullah, Kifayat
Haider, Shahab
Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images
title Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images
title_full Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images
title_fullStr Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images
title_full_unstemmed Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images
title_short Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images
title_sort hybrid sharpening transformation approach for multifocus image fusion using medical and nonmedical images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684527/
https://www.ncbi.nlm.nih.gov/pubmed/34931139
http://dx.doi.org/10.1155/2021/7000991
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