G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat
In order to solve the problem of large image noise and unremarkable features caused by factors such as fluctuations in the power of a light source during the terahertz image acquisition of wheat, this paper proposes a THz image-denoising model called G-RRDB. Firstly, a module called Ghost-LKA is pro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/12/15/2819 |
| _version_ | 1797586847399936000 |
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| author | Yuying Jiang Xinyu Chen Hongyi Ge Mengdie Jiang Xixi Wen |
| author_facet | Yuying Jiang Xinyu Chen Hongyi Ge Mengdie Jiang Xixi Wen |
| author_sort | Yuying Jiang |
| collection | DOAJ |
| description | In order to solve the problem of large image noise and unremarkable features caused by factors such as fluctuations in the power of a light source during the terahertz image acquisition of wheat, this paper proposes a THz image-denoising model called G-RRDB. Firstly, a module called Ghost-LKA is proposed by combining a large kernel convolutional attention mechanism module with a Ghost convolutional structure, which improves the characteristics of the network to acquire a global sensory field. Secondly, by integrating a spatial attention mechanism with channel attention, an attention module called DAB is proposed to enhance the network’s attention to important features. Thirdly, the Ghost-LKA module and DAB module are combined with the baseline model, thus proposing the dense residual denoising network G-RRDB. Compared with traditional denoising networks, both the PSNR and SSIM are improved. The prediction accuracy of G-RRDB is verified through the classification of the VGG16 network, achieving a rate of 92.8%, which represents an improvement of 1.7% and 0.2% compared to the denoised images obtained from the baseline model and the combined baseline model with the DAB module, respectively. The experimental results demonstrate that G-RRDB, a THz image-denoising model based on dense residual structure for moldy wheat, exhibits excellent denoising performance. |
| first_indexed | 2024-03-11T00:27:55Z |
| format | Article |
| id | doaj.art-f9b55e1cc90e4298b6700a2740f68908 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-11T00:27:55Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-f9b55e1cc90e4298b6700a2740f689082023-11-18T22:53:42ZengMDPI AGFoods2304-81582023-07-011215281910.3390/foods12152819G-RRDB: An Effective THz Image-Denoising Model for Moldy WheatYuying Jiang0Xinyu Chen1Hongyi Ge2Mengdie Jiang3Xixi Wen4Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaIn order to solve the problem of large image noise and unremarkable features caused by factors such as fluctuations in the power of a light source during the terahertz image acquisition of wheat, this paper proposes a THz image-denoising model called G-RRDB. Firstly, a module called Ghost-LKA is proposed by combining a large kernel convolutional attention mechanism module with a Ghost convolutional structure, which improves the characteristics of the network to acquire a global sensory field. Secondly, by integrating a spatial attention mechanism with channel attention, an attention module called DAB is proposed to enhance the network’s attention to important features. Thirdly, the Ghost-LKA module and DAB module are combined with the baseline model, thus proposing the dense residual denoising network G-RRDB. Compared with traditional denoising networks, both the PSNR and SSIM are improved. The prediction accuracy of G-RRDB is verified through the classification of the VGG16 network, achieving a rate of 92.8%, which represents an improvement of 1.7% and 0.2% compared to the denoised images obtained from the baseline model and the combined baseline model with the DAB module, respectively. The experimental results demonstrate that G-RRDB, a THz image-denoising model based on dense residual structure for moldy wheat, exhibits excellent denoising performance.https://www.mdpi.com/2304-8158/12/15/2819moldy wheatterahertz imageimage denoisingdense residual structure |
| spellingShingle | Yuying Jiang Xinyu Chen Hongyi Ge Mengdie Jiang Xixi Wen G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat Foods moldy wheat terahertz image image denoising dense residual structure |
| title | G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat |
| title_full | G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat |
| title_fullStr | G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat |
| title_full_unstemmed | G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat |
| title_short | G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat |
| title_sort | g rrdb an effective thz image denoising model for moldy wheat |
| topic | moldy wheat terahertz image image denoising dense residual structure |
| url | https://www.mdpi.com/2304-8158/12/15/2819 |
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