Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images
The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently un...
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2024-12-01
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| author | Jiayue Yan Chenglong Tao Yuan Wang Jian Du Meijie Qi Zhoufeng Zhang Bingliang Hu |
| author_facet | Jiayue Yan Chenglong Tao Yuan Wang Jian Du Meijie Qi Zhoufeng Zhang Bingliang Hu |
| author_sort | Jiayue Yan |
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| description | The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used. |
| format | Article |
| id | doaj-art-7b7dfd3195da44f985421e472cb7337c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7b7dfd3195da44f985421e472cb7337c2025-01-10T13:15:09ZengMDPI AGApplied Sciences2076-34172024-12-0115132110.3390/app15010321Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral ImagesJiayue Yan0Chenglong Tao1Yuan Wang2Jian Du3Meijie Qi4Zhoufeng Zhang5Bingliang Hu6Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaTangdu Hospital of Air Force Medical University, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, ChinaThe inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used.https://www.mdpi.com/2076-3417/15/1/321spatial dimensional push-broom hyperspectral imaging systemhyperspectral brain tumor dataimage denoisingnon-uniformity correction |
| spellingShingle | Jiayue Yan Chenglong Tao Yuan Wang Jian Du Meijie Qi Zhoufeng Zhang Bingliang Hu Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images Applied Sciences spatial dimensional push-broom hyperspectral imaging system hyperspectral brain tumor data image denoising non-uniformity correction |
| title | Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images |
| title_full | Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images |
| title_fullStr | Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images |
| title_full_unstemmed | Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images |
| title_short | Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images |
| title_sort | application of enhanced weighted least squares with dark background image fusion for inhomogeneity noise removal in brain tumor hyperspectral images |
| topic | spatial dimensional push-broom hyperspectral imaging system hyperspectral brain tumor data image denoising non-uniformity correction |
| url | https://www.mdpi.com/2076-3417/15/1/321 |
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