Wavelet-Based Enhanced Medical Image Super Resolution
Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem,...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9000539/ |
| _version_ | 1818323701487108096 |
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| author | Farah Deeba She Kun Fayaz Ali Dharejo Yuanchun Zhou |
| author_facet | Farah Deeba She Kun Fayaz Ali Dharejo Yuanchun Zhou |
| author_sort | Farah Deeba |
| collection | DOAJ |
| description | Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method. Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT). Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail. For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor. The mapping between the wavelet sub-band image and its approximate image is then determined. In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed. Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance. |
| first_indexed | 2024-12-13T11:16:53Z |
| format | Article |
| id | doaj.art-acc4c7e300df478c9fb69e25470d3b23 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:16:53Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-acc4c7e300df478c9fb69e25470d3b232022-12-21T23:48:36ZengIEEEIEEE Access2169-35362020-01-018370353704410.1109/ACCESS.2020.29742789000539Wavelet-Based Enhanced Medical Image Super ResolutionFarah Deeba0https://orcid.org/0000-0002-1987-2402She Kun1https://orcid.org/0000-0002-3942-5079Fayaz Ali Dharejo2https://orcid.org/0000-0001-7685-3913Yuanchun Zhou3https://orcid.org/0000-0003-2144-1131School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaComputer Network Information Center, Chinese Academy of Sciences, Beijing, ChinaComputer Network Information Center, Chinese Academy of Sciences, Beijing, ChinaLow-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method. Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT). Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail. For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor. The mapping between the wavelet sub-band image and its approximate image is then determined. In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed. Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance.https://ieeexplore.ieee.org/document/9000539/Medical imagessuper-resolution (SR)deep learningwavelet learning |
| spellingShingle | Farah Deeba She Kun Fayaz Ali Dharejo Yuanchun Zhou Wavelet-Based Enhanced Medical Image Super Resolution IEEE Access Medical images super-resolution (SR) deep learning wavelet learning |
| title | Wavelet-Based Enhanced Medical Image Super Resolution |
| title_full | Wavelet-Based Enhanced Medical Image Super Resolution |
| title_fullStr | Wavelet-Based Enhanced Medical Image Super Resolution |
| title_full_unstemmed | Wavelet-Based Enhanced Medical Image Super Resolution |
| title_short | Wavelet-Based Enhanced Medical Image Super Resolution |
| title_sort | wavelet based enhanced medical image super resolution |
| topic | Medical images super-resolution (SR) deep learning wavelet learning |
| url | https://ieeexplore.ieee.org/document/9000539/ |
| work_keys_str_mv | AT farahdeeba waveletbasedenhancedmedicalimagesuperresolution AT shekun waveletbasedenhancedmedicalimagesuperresolution AT fayazalidharejo waveletbasedenhancedmedicalimagesuperresolution AT yuanchunzhou waveletbasedenhancedmedicalimagesuperresolution |