# Research on Image Denoising in Edge Detection Based on Wavelet Transform
## 6 Conclusions  
In many cities in China, especially in the southern region, as a kind of infrastructure, bridges always affect the basic operation and economic development of the city. In order to meet the daily maintenance requirements of the bridge, we can extract the edges of bridges from maintenance images to obtain a lot of useful information. In the process of edge extraction, the edges of the original images are often interspersed with a lot of noise, which affects the edge extraction. In this paper, the wavelet transform is used to remove the noise from the images before edge detection, which effectively improves the effect of bridge image edge detection. The detailed conclusions are as follows.  
1. A bridge image taken in Wenzhou was chosen for study and analysis. The wavelet transform was used to denoise the image before extracting the bridge edges to improve the signal-to-noise ratio of the image. In the wavelet transform, four wavelet functions (sym5, db5, coif5 and fk6) and four decomposition levels (two, three, four and five levels) were used to decompose the image, followed by coefficient filtering and reconstruction to obtain the denoised image. The results show that the sym5 wavelet function performs best in the three-level decomposition, with _PSNR_ and _MSE_ of 23.48 dB and 299.49, respectively. The canny algorithm was used to detect the edges of the images before and after denoising, and it is obvious that the edge detection of the images was better after wavelet transform denoising.
2. In addition, the improvements of edge detection by Gaussian filtering and wavelet transform were compared and discussed. The Pratt quality factors of the two methods were 0.47 and 0.53, respectively, which shows that wavelet transform noise removal has better quality factors compared to Gaussian filtering. Thus, the effect of edge detection was improved. In summary, the use of wavelet transform to remove noise can provide a favorable method for edge detection and can lay a solid basis for the daily maintenance of bridges and related scientific research work.  
In this paper, color bridge images taken by UAVs were converted to monochrome images in order to verify the effectiveness of the wavelet transform. However, in many scenarios, the user would prefer to perform the processing with colored images. In addition to this, with the increasing superiority of technical tools, such as neural networks and deep learning, it will be the next research direction of the authors to apply the wavelet transform and the above two techniques to the edge detection of colored images.  
Conceptualization, N.Y. and L.H.; data processing, N.Y. and L.H.; writing--original draft preparation, N.Y.; writing--review and editing, N.Y., L.H. and W.S.; supervision and project administration, D.Z. and W.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The bridge photograph datasets were freely provided by Yangtze Delta Institute of Urban Infrastructure.  
The authors would like to thank the editors and the anonymous reviewers for their valuable suggestions.  
The authors declare no conflict of interest.