Research on Image Denoising in Edge Detection Based on Wavelet Transform
Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet tran...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1837 |
| _version_ | 1827760322282258432 |
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| author | Ning You Libo Han Daming Zhu Weiwei Song |
| author_facet | Ning You Libo Han Daming Zhu Weiwei Song |
| author_sort | Ning You |
| collection | DOAJ |
| description | Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The <i>PSNR</i> and <i>MSE</i> of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the <i>PSNR</i> and <i>MSE</i> of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results. |
| first_indexed | 2024-03-11T09:51:57Z |
| format | Article |
| id | doaj.art-55ae9f2889b844a3b05466d0c080bef2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:51:57Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-55ae9f2889b844a3b05466d0c080bef22023-11-16T16:11:08ZengMDPI AGApplied Sciences2076-34172023-01-01133183710.3390/app13031837Research on Image Denoising in Edge Detection Based on Wavelet TransformNing You0Libo Han1Daming Zhu2Weiwei Song3Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaPLA Army Academy of Artillery and Air Defense, Zhengzhou 450000, ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaPhotographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The <i>PSNR</i> and <i>MSE</i> of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the <i>PSNR</i> and <i>MSE</i> of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results.https://www.mdpi.com/2076-3417/13/3/1837edge detectionwavelet transformwavelet functioncannyPratt quality factor |
| spellingShingle | Ning You Libo Han Daming Zhu Weiwei Song Research on Image Denoising in Edge Detection Based on Wavelet Transform Applied Sciences edge detection wavelet transform wavelet function canny Pratt quality factor |
| title | Research on Image Denoising in Edge Detection Based on Wavelet Transform |
| title_full | Research on Image Denoising in Edge Detection Based on Wavelet Transform |
| title_fullStr | Research on Image Denoising in Edge Detection Based on Wavelet Transform |
| title_full_unstemmed | Research on Image Denoising in Edge Detection Based on Wavelet Transform |
| title_short | Research on Image Denoising in Edge Detection Based on Wavelet Transform |
| title_sort | research on image denoising in edge detection based on wavelet transform |
| topic | edge detection wavelet transform wavelet function canny Pratt quality factor |
| url | https://www.mdpi.com/2076-3417/13/3/1837 |
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