# Fully Symmetric Convolutional Network for Effective Image Denoising
## 3 Deep Symmetric Convolutional-Deconvolutional Network
### Network Performance  
Interpreting and understanding the behavior of deep neural networks remains one of the main challenges in deep learning. It is necessary to compare it with other known networks to understand new subjects and to interpret. From this perspective, comparative study enables determining the performance of various networks in terms of PSNR.  
In this section, we compare variations of the CNN architecture based on the patterns of layers. The combination of convolution and deconvolution networks contains 15 layers [29; 38] but there is no conclusive evidence to show which network performs better both quantitatively and qualitatively. To our knowledge, very few have used batch normalization for FCN-based image denoising.  
We have classified existing networks into four different models and compared them with our proposed network. A series of experiments were conducted to analyze the results quantitatively using PSNR values. Experiments were performed on a Kaggle dataset with 256 \(\times\) 256 resolution for seven different architectures with monochrome images. The dataset contained clean and noiseless images, and therefore, a noise process was integrated into the training procedure. The architectures were categorized into different models as shown in Table 1. The 8-bit integer intensity values of the dataset (values from 0 to 255) were normalized to 1 for faster convergence, and an Adam optimizer was adopted for all architectures.  
In the Convolution (C) network model, the image denoising task is formulated as a learning problem to train the convolution network. The convolution network acts as good feature extractor by which the goal of denoising is accomplished. However, some details in the denoised image are absent. In the Deconvolution (D) network model, the network provides a framework that permits the unsupervised construction of hierarchical image representations. Using the same parameters for learning each layer, the deconvolutional network can automatically extract rich features while denoising the image. This network suppresses noisy activations, makes blurry images sharper,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SL.no** & **Model** & **Architecture** & **PSNR** \\ \hline
1 & C & CCCCC CCCC & 31.51 \\
2 & D & DDDDDDDD & 32.00 \\
3 & BN & DBDBD BDBDB & 30.14 \\
4 & & CBCBC BCBCB & 30.31 \\
5 & & CCCCC DDDDD & 32.03 \\
6 & CD & DDDDDD CCCC & 31.68 \\
7 & & **CDC DCDCD** & **32.70** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network Performance.  
and captures details in an image. The batch normalization (BN) model helps to overcome the internal covariate shift problem but the network faces the serious issue of overfitting and makes large oscillations after few epochs for a small learning rate (\(\eta\) = 0.01). Poor results demonstrate that this architecture is inefficient for denoising compared with other architectures.  
The CD network model is composed of convolution and deconvolution layers. Two different architectures [((CCCCC DDDDD), (DDDDD CCCCC))] are experimented with in the CD network model. These architectures have been proposed for semantic segmentation [29; 38] comprising 13 layers with pooling and unpooling operations. This architecture is emulated in our experiments for denoising with different numbers of layers, and their results are tabulated. It is evident that these architectures give better results compared to other architectures. However, loss of details while denoising the heavily corrupted images remains a major issue.  
Therefore, we proposed a new architecture (CDCDC DCDCD) and performed similar experiments. The proposed architecture not only exhibited better PSNR values but also recovered images with subtle loss of details and by eliminating the corruptions.