# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 1 Introduction
### Motivation and Contributions of Research Work  
People are working to embed filtering techniques in machine learning models for visual analysis, especially deep learning. To our knowledge, how this embedded technique works compared to its traditional denoising counterpart is yet to be performed. This research gap motivates us to perform a comparative study between traditional and embedded denoising. We choose to analyze the performance of CNN for IoT image recognition applications using different types of environmental impacts such as rain, shadow, snow, darkness, and exposure. Camera impacts include lens blur and lens dirtiness. As expected, denoising does not work for all types of impacts because all denoising techniques consider the basic conceptual models for image filtering that are developed exploiting the characteristics of general noises such as white Gaussian noise and salt and pepper noise. These filtering techniques are not generic enough to encode the characteristics of all types of impacts, raising the question of whether we should apply a denoising approach for a particular impact. If the use of the denoising approach is decided, what type of denoising approach will be adopted? To address this research issue, this study also proposes a conceptual approach that will help in deciding whether and which type of denoising approach should be applied for a particular impact in a recognition dataset. The major contributions of the paper are as follows:  
1. We filter image noise from training and test data sets using traditional methods of filtering noise such as median filter and Gaussian filter.
2. We embed a layer for image denoising in the CNN model. Then, we compare the traffic sign and general object recognition accuracy and processing time for the CNNalgorithm with the traditional approach of denoising and embedded denoising against a baseline approach called without denoising.
3. For the detection accuracy and computational time performance, we use challenging unreal and real environments for traffic sign recognition (CURE-TSR) [18] and challenging unreal and real environments for object recognition (CURE-OR) [19] datasets. We use environmental impacts such as rain, shadow, darkness, and snow. For camera impacts, we cover lens blur and lens dirtiness from both CURE-TSR and CURE-OR datasets. We utilize impacts such as contrast, salt and pepper noise, overexposure, and underexposure, from the CURE-OR dataset. The recognition accuracy of CNN for the traditional denoising approach shows superior performance to that when image denoising is embedded in CNN.
4. To decide whether denoising would be adopted, we calculate the distribution (histogram) of PSNRs of the images of the dataset affected by impacts before and after denoising. Through PSNR histograms, we assess whether the quality of images has been improved based on our developed two principles. The histograms were produced using the two data sets for all impact types mentioned in Contribution 3 for the median and Gaussian filters. When the overall quality of images is improved after denoising, these histograms support the adoption of filtering for CNN-based image recognition.  
The rest of this paper is organized as follows. Related works are mentioned in Section 2. Section 3 elaborates on our comparative performance study and an approach to decide whether denoising will be adopted. Section 4 gives comprehensive experimental results with the comparative approach. Conclusions are drawn in Section 5.