# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 2 Literature Review
### Approaches Embedding Image Denoising in Deep Learning  
In image processing, neural networks are one of the most promising approaches to denoising images. There are various types of DL [7; 44; 45] architectures available for image denoising. A fully symmetric convolutional-deconvolutional network (FSCN) is presented for image denoising in [15]. The proposed model consists of a chain of sequential symmetric convolutional-deconvolutional layers. End-to-end, this framework learns convolutional-deconvolutional mappings from corrupted images to clean ones without using image priors. With the convolutional layers, the image content is encoded while corruptions are removed. With the deconvolutional layers, it is decoded so that image content details can be retrieved. The reconstruction loss is minimized by an adaptive moment optimizer, which is suitable for large datasets and noisy images. A comprehensive evaluation of the FSCN model against existing state-of-the-art denoising algorithms was conducted. As a result, the proposed model achieves superior denoising, both qualitatively and quantitatively. However, in this model, because of the application of a series of symmetric convolutional-deconvolutional layers, there could be a considerable amount of pertinent information loss even though reconstruction is minimized. And the number of symmetric convolutional-deconvolutional layers is not determined by minimizing loss. Moreover, the denoised images have not been used to test the performance of a deep learning algorithm like CNN for a particular application. The approach introduced in [16] presents a novel deep CNN for image denoising, which can directly obtain a clean image from a noisy one. For image denoising, batch renormalization (BRN) is used, which can handle small mini-batch problems. Furthermore, BRN can also accelerate the convergence of training the network without requiring any specific hardware platform. For this, it is a good choice to combine BRN and CNN for image denoising on low-configuration hardware devices. The performance of image denoising is improved by residual learning. The batch-renormalization denoising network (BRDNet) is robust to both synthetic and real noisy images, according to experimental results. However, the effectiveness of this method is yet to be explored for low-light and blurred images. Also, this method requires resizing images, which results in a loss of information. To address the information loss issue mentioned in [15], a novel method for denoising images is proposed by [PERSON] et al. [17] to assist intelligent robot welding. To extract and accumulate multi-scale feature maps, an attention-dense convolutional block is proposed. To learn long-range spatial contexts from local feature maps, a residual bi-directional conv long short-term memory (ConvLSTM) block is proposed. The experimental results prove that the proposed image denoising network could correctly extract the laser stripes from seam images. However, all the above-mentioned deep learning-based denoising models require the resizing of images, which could lead to the loss of information or the addition of redundant information in the images. Consequently, this could impact many applications like event and object detection and classification accuracy. For achieving effective and efficient real image denoising, the advantages of two networks--(i) CNN and (ii) transformer--are merged in this paper [46]. A hybrid denoising model based on the transformer encoder and convolutional decoder network (TEDNet) is proposed. Transform using radial basis functions (RBFs) attention is used as an encoder to improve overall model representation. To reduce the computational complexity of the entire denoising network, residual CNNs are used instead of transformers. With relatively low computational costs, TEDNet achieves state-of-the-art denosing performance on real images. Similarly, this paper [47] proposes a novel and effective network architecture based on the transformer TC-Net.