# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 1 Introduction  
In the transportation, agriculture, and defence sectors, weather phenomena can have various negative consequences [1, 2, 3, 4]. In these sectors, images are captured in the outdoor environment. When images are acquired, compressed, and transmitted, noise is inherently introduced by the environment, camera, and other factors, resulting in distortion and loss of information. With the presence of noise, image processing tasks, such as object recognition and segmentation, edge detection, and feature extraction are adversely affected [5]. This is because the contrast, edges, textures, object details, and quality of a noisy image are impacted, lowering the post-processing algorithm's performance [6]. Therefore, image denoising plays an important role in modern image processing systems.  
Image denoising is used to remove noise from a noisy image, to restore the true image. As noise, edge, and texture are high-frequency components; it is difficult to distinguish them in the denoising process, and some details may be lost as a result. In general, recovering meaningful information from noisy images to obtain high-quality images has become an increasingly important problem [7].  
Different kinds of widely adopted image-denoising filters are used to remove noise from the images impacted by various environmental and camera parameters [8]. The widely used image noise denoising techniques are median [9], Gaussian [10], and bilateral filters [11; 12]. Various traditional approaches exist for image noise removal where images are denoised as a preprocessing step. For example, for object detection, firstly, images are denoised using a depth filter. Secondly, objects are recognized using the convolutional neural network (CNN) [13]. Another example is [14], where images are pre-processed with Gaussian blur. The simulation of a self-driving car has been carried out, which can learn to drive autonomously without manual intervention of human beings using deep CNN. A few other approaches of deep learning such as CNN having embedded image denoising are available in the current literature. Examples of these techniques are [15; 16; 17]. Images collected from different sources may have different sizes. Since the size of the convolution kernel of CNNs is fixed in CNN-based deep learning techniques, the resizing of images while denoising is required, which leads to the loss of information in the images. The image resizing process may involve both downscaling and upscaling images. While downscaling results in image information being lost, there is the possibility of the addition of redundant information in images during upscaling as well. To our knowledge, no comparative performance analysis study on the performance of CNNs for traditional and embedded image denoising exists in the current literature. Besides the performance study presented in this paper, the results show that denoising cannot produce superior recognition accuracy for some impacts such as shadow and darkness compared with \"without denoising\". This insight raises the question of whether we should adopt denoising for the dataset affected by a particular impact. To address this research question, we calculate the distribution of the PSNRs of all images of a dataset before and after denoising. We devise two principles that help to decide whether the overall quality of images has been improved after denoising.