# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 4 Results and Discussion
### Decision about Denoising Needs to Be Made  
We compare the recognition accuracy with the quality of images through PSNR histograms. Various image quality assessment metrics exist such as PSNR, the structural similarity index measure (SSIM), and others to assess image quality. However, among them, PSNR is one of the most reliable and widely used image quality assessment metrics. As demonstrated in Figure 8, the recognition accuracy varies consistently with the PSNR values, and in general, accuracy increases with an increased PSNR value. To illustrate this, in lens blur with Gaussian filtering, the traffic sign recognition accuracy decreases from 87.6 to 85.3 as PSNR decreases from 31.83 to 28.05. Therefore, to compare the quality of images, we used the PSNR of all images for a particular impact before and after Gaussian and median filtering through histograms. Note that each image has one PSNR value. As per the discussion of results from the CNN's recognition accuracy, we realize that denoising with Gaussian and median filters decreases the recognition accuracy for some environmental and camera impacts compared to applying denoising in CNN. For example, in rain, shadow, and darkness, the recognition accuracy decreases with denoising approaches. The decrease in recognition accuracy shows that the application of Gaussian and median filters degrades the image quality rather than improving it. Therefore, we need some criteria to make a firm decision on whether we should use denoising or not. We make a decision on assessing the image quality. If the image quality improves after using embedded and traditional denoising with Gaussian and median filters, then denoising works; otherwise, we should not adopt the particular method for denoising. As alluded to before, we choose PSNR as an image quality measure and the distribution (histogram) of PSNRs to show the comparative image quality with and without applying denoising. Figure 9a shows the comparative histogram of PSNR values of underexposure before and after Gaussian denoising for the CURE-OR dataset. According to the two principles mentioned in Section 3.2, the histogram is right skewed and has higher frequency values for higher PSNR values such as 28.2 dB and 28.3 dB after Gaussian denoising. The right-skewed and higher frequency values of higher PSNRs indicate that the overall image quality of the CURE-OR dataset in underexposure is improved after denoising with Gaussian filtering, i.e., the Gaussian filter is effective for the removal of underexposure. Parallely, Table 3 also shows that the highest (50.7%) and second highest (45.9%) object recognition accuracies produced by CNN are for embedded and traditional denoising with Gaussian filtering, respectively. Similarly, Figure 9b represents the histogram of PSNR values in underexposure before and after median denoising for the CURE-OR dataset. In comparison to Gaussian denoising, in this figure, the histogram is right-skewed (as it is shifted to a higher PSNR value, which is 28.3 dB) after denoising. However, higher frequency values do not exist for higher PSNRs (28.1 and 28.2 dB) after denoising. Only one principle is followed for the median filter, unlike the Gaussian filter, which follows both principles. To conclude,  
Figure 8: Mean PSNR vs. recognition accuracy in CURE-TSR dataset. Here, LBWD: lens blur without denoising; LBM: lens blur with median filtering; LBG: lens blur with Gaussian filtering; LDWD: lens dirty without denoising; LDM: lens dirty with median filtering; LDG: lens dirty with Gaussian filtering; RWD: rain without denoising; RM: rain with median filtering; RG: rain with Gaussian filtering; SWD: shadow without denoising; SM: shadow with median filtering; SG: shadow with Gaussian filtering; DWD: darkness without denoising; DM: darkness with median filtering; and DG: darkness with Gaussian filtering. it is reflected from these histograms that the Gaussian filter is effective for the removal of underexposure.