Wavelets and curvelets transform for image denoising to damage identification of thin plate
As a common structural form, thin plates are widely used in civil engineering. Since the thin plate needs to face harsh work conditions, the damage inevitably to be accumulated, thus affecting the stability and safety of the application components. Therefore, it is of great application significance...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123022005072 |
| _version_ | 1797894667300241408 |
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| author | Deng Yulong Ding Ke Ouyang Chunsheng Luo Yingshe Tu Yu Fu Jianyi Wang Wei Du Yaguang |
| author_facet | Deng Yulong Ding Ke Ouyang Chunsheng Luo Yingshe Tu Yu Fu Jianyi Wang Wei Du Yaguang |
| author_sort | Deng Yulong |
| collection | DOAJ |
| description | As a common structural form, thin plates are widely used in civil engineering. Since the thin plate needs to face harsh work conditions, the damage inevitably to be accumulated, thus affecting the stability and safety of the application components. Therefore, it is of great application significance to quantify and characterize the damage of thin plates. However, the raw images produced by current inspection techniques such as Ultrasonic immersion C-scan technology, Metal sheet Lamb wave inspection technology, etc applied to thin plates usually bring various noises and imperfections during the reception, encoding, and transmission. In this paper, wavelet transform and Curvelet transform are used to denoise the detected noise image. First, we outline the numerical implementation of two newly developed multi-scale representation systems. Curvelet transform is a new multi-scale transform based on wavelet transform after 1999. The purpose of this paper is to analyze the influence of wavelet and Curvelet transform on image denoising. These methods can also be applied to the problem of image restoration from noisy images, and the effects of denoising on images are compared. The results show that the Curvelet transform can accurately identify the damage location for the thin plate damage degree, damage range, strip damage, and multiple damage conditions, and its energy focusing is better than that of the wavelet transform in each type of thin plate damage. |
| first_indexed | 2024-04-10T07:14:06Z |
| format | Article |
| id | doaj.art-7149a3327c9247bbbff4d603e7f8b420 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-10T07:14:06Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-7149a3327c9247bbbff4d603e7f8b4202023-02-26T04:27:50ZengElsevierResults in Engineering2590-12302023-03-0117100837Wavelets and curvelets transform for image denoising to damage identification of thin plateDeng Yulong0Ding Ke1Ouyang Chunsheng2Luo Yingshe3Tu Yu4Fu Jianyi5Wang Wei6Du Yaguang7Hunan Province Key Laboratory of Engineering Rheology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; College of Civil Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 410004, ChinaHunan Province Key Laboratory of Engineering Rheology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; College of Civil Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; Corresponding author. Hunan Province Key Laboratory of Engineering Rheology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China.Hunan Province Key Laboratory of Engineering Rheology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; College of Civil Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; Hunan Institute of Traffic Engineering, Hengyang, 421000, China; Corresponding author. Hunan Province Key Laboratory of Engineering Rheology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China.Hunan Province Key Laboratory of Engineering Rheology, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; College of Civil Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China; Hunan Institute of Traffic Engineering, Hengyang, 421000, ChinaHunan Institute of Traffic Engineering, Hengyang, 421000, ChinaHunan Institute of Traffic Engineering, Hengyang, 421000, ChinaHunan Institute of Traffic Engineering, Hengyang, 421000, ChinaWuhan Municipal Engineering Design & Research Institute Co., Ltd., Wuhan, Hubei, 430015, ChinaAs a common structural form, thin plates are widely used in civil engineering. Since the thin plate needs to face harsh work conditions, the damage inevitably to be accumulated, thus affecting the stability and safety of the application components. Therefore, it is of great application significance to quantify and characterize the damage of thin plates. However, the raw images produced by current inspection techniques such as Ultrasonic immersion C-scan technology, Metal sheet Lamb wave inspection technology, etc applied to thin plates usually bring various noises and imperfections during the reception, encoding, and transmission. In this paper, wavelet transform and Curvelet transform are used to denoise the detected noise image. First, we outline the numerical implementation of two newly developed multi-scale representation systems. Curvelet transform is a new multi-scale transform based on wavelet transform after 1999. The purpose of this paper is to analyze the influence of wavelet and Curvelet transform on image denoising. These methods can also be applied to the problem of image restoration from noisy images, and the effects of denoising on images are compared. The results show that the Curvelet transform can accurately identify the damage location for the thin plate damage degree, damage range, strip damage, and multiple damage conditions, and its energy focusing is better than that of the wavelet transform in each type of thin plate damage.http://www.sciencedirect.com/science/article/pii/S2590123022005072Thin plateWavelet transformCurvelet transformImage denoisingDamage identification |
| spellingShingle | Deng Yulong Ding Ke Ouyang Chunsheng Luo Yingshe Tu Yu Fu Jianyi Wang Wei Du Yaguang Wavelets and curvelets transform for image denoising to damage identification of thin plate Results in Engineering Thin plate Wavelet transform Curvelet transform Image denoising Damage identification |
| title | Wavelets and curvelets transform for image denoising to damage identification of thin plate |
| title_full | Wavelets and curvelets transform for image denoising to damage identification of thin plate |
| title_fullStr | Wavelets and curvelets transform for image denoising to damage identification of thin plate |
| title_full_unstemmed | Wavelets and curvelets transform for image denoising to damage identification of thin plate |
| title_short | Wavelets and curvelets transform for image denoising to damage identification of thin plate |
| title_sort | wavelets and curvelets transform for image denoising to damage identification of thin plate |
| topic | Thin plate Wavelet transform Curvelet transform Image denoising Damage identification |
| url | http://www.sciencedirect.com/science/article/pii/S2590123022005072 |
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