# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 5 Conclusions  
In this paper, we present traditional and embedded image-denoising approaches to analyze the performance of a deep learning algorithm called CNN. The performance of CNN is analyzed in terms of recognition accuracy and computational time. For this study, we use the CURE-TSR and CURE-OR datasets. We embed the Gaussian and median denoising filters because with the traditional approach, the results are almost the same in median and bilateral filters. Therefore, the Gaussian and median filters are used as these are more time-efficient among all three filters. The traditional denoising approach achieves high traffic recognition accuracy for more impacts than the embedded-based denoising approach. And the higher computational time is taken by both denoising approaches with a median filter compared to the Gaussian filter. The Gaussian filter is the most time-efficient filter to be used in denoising approaches. In this paper, for denoising embedded in CNN, we added an extra denoising layer. It is embedded as the first layer in the existing CNN model. However, we can add a denoising layer in any place before or after each convolution layer as well and will evaluate the results in the future. Moreover, the performance of denoising approaches has been evaluated using recognition accuracy. We present an approach to deciding whether decisioning will be adopted by leveraging the PSNRs distribution of images. The derived decisions verified the recognition accuracy for all the impacts of the two well-known image recognition datasets used in this paper. In the future, we aim to assess the impact of image denoising on various CNN-based image analysis applications such as smart agriculture and others where outdoor image capturing is required. This performance study has been conducted using traditional denoising approaches such as Gaussian and median filters. In this performance study, we embedded the filters into the CNN model, and these above-mentioned filters are used as a pre-processing step before finding the recognition accuracy using CNN in traditional denoising. CNN is mainly focused on calculating the traffic sign and object recognition accuracy. However, there are DL-based techniques especially using CNN dedicated to denoising that are available in the literature. For our future study, we will exploit these DL-based denoising techniques. In  
Figure 12: Comparative histogram of PSNR of darkness without denoising and with Gaussian and median filtering for CURE-TSR dataset. (**a**) Before and after Gaussian filtering. (**b**) Before and after median filtering.  
this study, we use CURE traffic signs and object recognition datasets. We will use other image recognition benchmark datasets applied in different applications in the future.  
Conceptualization, R.K. and M.I.; Methodology, R.K.; Software, R.K.; Validation, R.K.; Formal analysis, R.K.; Investigation, R.K.; Writing--original draft, R.K.; Writing--review & editing, R.K., G.K. and M.I.; Visualization, R.K.; Supervision, G.K. and M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.