# Median Filter Aided CNN Based Image Denoising:
## 4 Experimental Results  
### Dataset  
For evaluating our image denoising ensemble based model, we use the BSD500 dataset, which was proposed by [PERSON] et al. in (2018) and it can be downloaded from  
[[https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)]([https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)) BSD500 download (accessed on 27 March 2021), and the Set12 dataset ([PERSON] et al., 2018), which is a collection of 12 widely used testing images available at [[https://www.researchgate.net/figure/12-images-from-Set12-dataset_fig11_338424598](https://www.researchgate.net/figure/12-images-from-Set12-dataset_fig11_338424598)]([https://www.researchgate.net/figure/12-images-from-Set12-dataset_fig11_338424598](https://www.researchgate.net/figure/12-images-from-Set12-dataset_fig11_338424598)) Set12 download (accessed on 27 March 2021). We use 200 gray scale images of the BSD500 dataset and resize each image to \((200)\times(200)\) for training our model. The BSD500 dataset is actually an extension of the BSDS300, in which the original 300 images are used for training and validation, and 200 fresh images, annotated by humans, are added for testing. We introduce additive white Gaussian noise in the images by setting the standard deviation of the Gaussian noise distribution to 15, 25, and 50, also known as noise levels. For training purpose, each image is converted into patches of size \((40)\times(40)\), which results in a total of 596 patches per image. The model is evaluated on the 200 testing images of the BSD500 dataset, whose results are illustrated in Table 1, and also on the 12 images from Set12 dataset whose results are illustrated in Table 2.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **Noise Level 15** & **Noise Level 25** & **Noise Level 50** \\ \hline TV-Chambolle [11] & 24.37 & 22.34 & 18.33 \\ \hline Wavelet-VisuShrink [9] & 21.38 & 19.78 & 16.99 \\ \hline Wavelet-BayesShrink [10] & 25.05 & 22.40 & 18.19 \\ \hline ADNet model & 31.55 & 28.87 & 25.90 \\ \hline IRCNN-model & 31.56 & 28.94 & 25.93 \\ \hline DrCNN-model & 31.64 & 28.85 & 26.08 \\ \hline ADNet(dilation rate = 8) + median layer & 31.63 & 29.12 & 25.98 \\ \hline IRCNN-model (dilation upto 6) + median layer & 31.60 & 29.08 & 26.08 \\ \hline DrCNN+ median layer & 31.66 & 29.08 & 26.10 \\ \hline Ensemble-model & **31.73** & **29.20** & **26.20** \\ \hline \end{tabular}
\end{table}
Table 1: Average PSNR values of images predicted by the image denoising models on the images of BSD500 dataset.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Denoising Models** & **01.** & **02.** & **03.** & **04.** & **05.** & **06.** & **07.** & **08.** & **09.** & **10.** & **11.** & **12.** \\ \hline \hline \multicolumn{11}{c}{Noise level of 15} \\ \hline TV-Chambolle [11] & 23.54 & 24.69 & 23.58 & 22.56 & 22.62 & 22.79 & 22.47 & 25.58 & 23.00 & 24.36 & 24.62 & 24.01 \\ \hline Wavelet-VisuShrink [9] & 19.77 & 22.32 & 19.11 & 18.81 & 18.05 & 19.28 & 18.81 & 21.94 & 19.82 & 20.95 & 21.20 & 20.93 \\ \hline Wavelet-BayesShrink [10] & 23.40 & 26.07 & 23.76 & 23.10 & 22.82 & 23.20 & 23.15 & 26.21 & 23.19 & 24.75 & 25.16 & 24.56 \\ \hline ADNet & 32.36 & 34.46 & 33.08 & 31.80 & 32.64 & 31.58 & 31.73 & 34.12 & 31.56 & 32.16 &