# Fully Symmetric Convolutional Network for Effective Image Denoising
## 4 Experimental Results
### Network Parameter Setting  
Although deeper networks tend to achieve better image restoration performance, problems related to different parameters still need to be investigated. A set of experiments is carried out with different network parameters: (a) filter number, (b) filter size, and (c) depth of the layers.  
Filters act as feature detectors and the significance of the number of feature detectors is related to the number of features that the network can potentially learn. Each filter generates a feature map that allows the network to learn the explanatory factors within the image. Filter numbers 8, 16, 32, and 64 with fixed filter size 3 \(\times\) 3 were chosen and the corresponding PSNR values were recorded on the validation set during training as shown in Figure 4. The figure shows that the filter number 64 achieved the best result in terms of PSNR values. However, a smaller number of filters is preferred for fast testing and suitable filter size has to be chosen to identify key features in the image. This helps to find the key features (edges, lines, shape, etc.) when convolved with appropriate kernels. Therefore, filter number 64 was chosen to study the PSNR output as a function of different filter size.  
Large filter size may result in large receptive fields but this renders the networks more difficult to train and converge to a poor optimum. Using a large filter size inevitably increases the complexity, overlooks the features, and possibly skips the essential details in the image. On the other hand, small filters are favored for high-level tasks [35] and beneficial for convergence in complex mappings. Thus, it is important to choose the most suitable size of the filter. Filter sizes \(2\times 2\), \(3\times 3\), \(4\times 4\), and \(5\times 5\) were chosen for our experiment. Figure 5 shows the PSNR values on the validation set while training. It is observed that the filter size \(3\times 3\) gave the optimum result for the given filter number 64, whereas the PSNR values drastically decreased for higher-sized filters.  
Experiments were also conducted for different depths of layers including 4, 6, 8, 10, 12, and 14, and PSNR values were recorded for validation. Figure 6 reveals that the performance increased gradually from layers 4 to 10 and began to decrease thereafter. Surprisingly, the PSNR dropped abruptly for a network with 14 layers and no information was recovered. Therefore, the 14-layer model was trained with filter number 32, filter size \(3\times 3\). The values obtained were still lower than values with the 10-layer model. Therefore, it is evident that a mere increase in layers without changing the parameters would actually degrade the performance. The reason for performance degradation may be due to the loss or corruption of image details with more convolution layers.  
Figure 4: PSNR values on the validation set during training with different filter numbers.  
Figure 5: PSNR values on the validation set during training with different filter size.  
Deeper networks in image restoration tasks tend to easily suffer from performance degradation due to gradient vanishing and become much harder to train. This is one of the common problems faced by researchers in neural networks [39]. The average PSNR values tested on the standard 10 images for different depth layers as shown in Figure 7 clearly indicates that the 10-layer model achieved better performance. Therefore, in all our experiments, the 10-layer model was used with filter number 64, filter size \(3\times 3\), with an Adam optimizer.