# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 2 Literature Review
### Image Denoising Techniques  
Widely used image-denoising filters are median, Gaussian, and bilateral filters [22]. Median filters are widely considered to be the most effective way to eliminate salt and pepper noises. Low-density impulse noise can be effectively eliminated by it [23]. Using the median filter, noise is removed from an image, and edge detection is improved. A positive odd integer is used for the kernel size [24]. Another filter is the Gaussian filter, which we use for image denoising. Gaussian filters smooth images more effectively [25]. It is based on Gaussian distribution. The probability density function (\(P(x)\)) of Gaussian distribution is represented by Equation (1). \[P(x)=\frac{1}{\sqrt{2n\sigma^{2}}}e^{-(x-\mu)^{2}/(2\sigma^{2})} \tag{1}\]  
Here, \(x\) is a grey level intensity of a pixel belonging to a window in an image. \(\mu\) is the mean pixel intensity value of all pixels within that window, and \(\sigma\) is the standard deviation [26]. The amount of smoothing of the Gaussian is determined by standard deviation \(\sigma\). The Gaussian filter takes the neighborhood around the pixel and finds its Gaussian weighted average. A Gaussian filter is based solely on space, that is, nearby pixels are taken into account during filtering. No consideration is given to pixels with nearly the same intensity. The algorithm does not take into account whether a pixel is an edge pixel or not. The edges are also blurred in the Gaussian filter. Wavelet denoising is also an effective tool for image denoising. However, it is efficient in dealing with additive white Gaussian noise (AWGN). The method may not work well for non-stationary noise or impulse noise without proper adaptation. The Wiener filter is also a fundamental and useful tool for denoising images. However, it has limitations when applied to real-world noisy images with complex characteristics. Recently, there has been a surge in the development of image denoising methods [27; 28; 29; 30; 31; 32] based on deep learning. To effectively remove noise from images, CNN-based denoising techniques use a large number of convolutional layers. ResNet [33] U-Net [34], and DenseNet [35] are typical examples of this type of architecture. Deeper CNNs often lead to vanishing/exploding gradients that can be alleviated by adding skip connections between neighboring layers. In U-Net, feature mapping is concatenated from the first to the last convolutional layer, as well as from the second to the second-last convolutional layer. Through skip connections, the input of the convolutional blocks (containing multiple convolutional layers) is added directly to their output. As a result of DenseNet, convolutional layers are connected to one another, which overcomes the limitations of ResNet in that some layers of information are selectively discarded. DBCN [36] extracts local and contextual information using a multibranch structure. The CMSC network [37] infers image features by cascading subnetworks. Due to a common loss function and a common input, these CNNs resemble wide networks, yet they are still deep networks. To overcome this issue, authors [38] presents a true wide CNN (WCNN) that takes advantage of the independence of wavelet decomposition and splits all convolutional layers initially used to train one image into numerous separate subnetworks. The WCNN is made up of numerous distinct subnetworks, each of which only serves to train the features of its own wavelet subband and has its own input, output, and loss functions. The author's goal was to provide a broad CNN framework to reorganize numerous convolutional layers and offer an innovative solution to the issue of vanishing gradients. Through wavelet decomposition, the authors divided a huge image denoising challenge into several smaller, independent denoising problems. For each issue, noise is eliminated from the subband in a certain scale, a certain direction, and a smaller size. In order to filter the noise, the authors combined batch normalization and residual learning with a CNN [28].