# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 4 Results and Discussion
### Recognition and Computational Time Analysis for CURE-TSR
#### 4.2.1 Traffic Sign Recognition Accuracy  
The recognition accuracy of CURE-TSR is mentioned in Table 3. The highest accuracy is given in bold in Tables 3 and 5. The table shows the traffic sign recognition accuracy with both denoising approaches (embedded and traditional) using median and Gaussian filters and no denoising. To illustrate, in lens blur, the maximum recognition accuracy comes with the traditional denoising technique using a median filter, which is 73.3% compared with 70.3% accuracy obtained by the Gaussian filter, because Gaussian filters blur or smooth the image, which results in more blurring of images. Hence, the median filter is more effective at denoising lens blur in comparison with the Gaussian filter. Contrasted to lens blur, the Gaussian filter is effective in lens dirty, which is clearly reflected in Table 3. The traffic sign recognition accuracy is 88.1% for the embedded denoising approach with the Gaussian filter, which is maximum, as the Gaussian filter results in smoothening of the image while preserving the overall structure in lens diritness. To perceive the overall detection accuracy and reliability of a particular approach, we calculate the mean value of Top-1 accuracies and their SD for all impacts. Overall, out of the without denoising, traditional, and embedded denoising approaches, traditional denoising with a median filter is superior to or similar to without denoising; it achieves maximum accuracy for the maximum number of impacts (2/6 impacts--lens blur and snow). Additionally, even though embedded denoising with median filter achieves the highest mean accuracy (76.5), traditional denoising with median filter obtains the second-highest mean accuracy (74.3) and the lowest SD (17.9) because the traditional denoising approach does not resize the images before denoising. However, the embedded approach requires the resizing of images before denoising, which leads to the loss of information and the addition of some redundant data into the existing database. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach Impact**} & \multicolumn{4}{c}{**Computational Time for Each Impact**} & \multicolumn{4}{c}{**Total**} \\ \cline{2-9}  & **Blur** & **Dirty** & **S \& P** & **CT** & **OE** & **UE** & **Mean** & **SD** \\ \hline Without denoising & 60.1 & 41.8 & 43.5 & 46.3 & 48 & 36.9 & **46.1** & **7.86** \\ \hline Embedded median & 40.4 & 42.4 & 43.8 & 60 & 67.8 & 45.4 & **49.9** & **11.19** \\ \hline Embedded Gaussian & 39.4 & 61.3 & 41.8 & 48.5 & 37.5 & **41.2** & **44.9** & **8.83** \\ \hline Traditional median & 64.9 & **39.3** & 118.4 & 64.8 & 47.6 & 65.5 & **66.7** & **27.55** \\ \hline Traditional Gaussian & **39.2** & 43 & **40.3** & **37.6** & **35.4** & 65.2 & **43.4** & **10.95** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational time (minutes) for object recognition for different impacts with various denoising approaches for CURE-OR. Here, mean and SD stand for the mean and standard deviation of the computational time for all impacts. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach Impact**} & \multirow{2}{*}{**Blur**} & \multirow{2}{*}{**Dirty**} & \multirow{2}{*}{**S \& P**} & \multirow{2}{*}{**CT**} & \multirow{2}{*}{**OE**} & \multirow{2}{*}{**UE**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} \\ \cline{1-1} \cline{6-10} Without Denoising & **52.6** & 35.84 & **53.7** & 55.8 & 43.5 & 42.4 & **47.3** & **7.2** \\ \hline Embedded median & 32.8 & 34.4 & 41.4 & 31.3 & 34.4 & 30.2 & **34** & **3.6** \\ \hline Embedded Gaussian & 49.1 & 46.2 & 50.4 & 55 & 46.5 & **50. On the other hand, no denoising technique works in shadow and darkness. Table 2 shows the mean and SD of a particular image under different levels of impacts. It is depicted in Table 2 that the mean of gray-level values decreases as the level of impact such as darkness and shadow increases.