# Boosting of Denoising Effect with Fusion Strategy
## 4 Experiments
### Datasets and Experimental Setup  
Referring to the two widely used test datasets and the ESPL synthetic image database [42], we derived our test images to evaluate the denoising performance of the proposed method and that of several competing methods. The first datasets contains ten natural images that are commonly used to study image denoising, including four images with a size of \(256\times 256\) (_Cameraman_, _House_, _Monarch_ and _Peppers_), and six images with a size of \(512\times 512\) (_Barbara_, _Boat_, _Couple_, _Hill_, _Lena_, and _Man_), as shown in Figure 4. The second one is a set of 50 natural images selected from the Berkeley segmentation dataset (BSD) [43]. The third dataset contains 25 high quality synthetic color images obtained from the Internet, which generally comprised 1920 \(\times\) 1080 pixels. The images are primarily selected from some popular animation movies and video games. All of the images contain both repetitive patterns and irregular textures. Some examples can be seen in Figure 5.  
We compare the denoising performance of our proposed method with that of seven state-of-the-art and representative denoising methods, including BM3D [5], NCSR [15], WNNM [7], PGPD [17], DnCNN [27], TWSC [14], and FFDNet [26]. The denoising results of all the competing algorithms are generated using the source codes released by their original authors, and we use the default parameters. To quantitatively evaluate the visual quality of the images denoised via the different methods, the assessing index peak signal to noise ratio (PSNR) is used. This is defined as follows:  
\[PSNR=\frac{255^{2}}{\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}(\mu_{i,j}-x_{i,j}) ^{2}}(\text{dB}) \tag{15}\]  
Figure 4: Ten commonly used testing images. (**a**) Barbara. (**b**) Boat. (**c**) Cameraman. (**d**) Couple. (**e**) Hill. (**f**) House. (**g**) Lena. (**h**) Man. (**i**) Monarch. (**j**) Peppers.  
Figure 5: (**a–d**) The 4 imagesin the ESPL database.  
where \(\mu_{i,j}\) and \(x_{i,j}\) represent the pixel values of the restored image and the original image respectively, and the size of the input image is \(M\times N\). We also calculated the structural similarity index measurement (SSIM) [44], the feature similarity index measurement (FSIM) [45], the visual information fidelity (VIF) [46] and the information content weighting SSIM (IW-SSIM) [47] of the competing methods. These metrics provide quality measurements closer to the characteristics of human vision, enabling further evaluation of the denoising performance. For all these aforementioned indexes, larger values indicate that the denoised images will appear more similar to their original ones in terms of human vision. The basic parameter setting is as follows: the number of images \(N\) is two and the pixel intensity \(I_{n}(x,y)\) is normalized to the range of [0, 1]. We conducted experiments to determine the best PSNR value with respect to changes in \(\alpha\) in the range of [0.25, 1.25]. The experimental results show that a larger \(\alpha\) leads to a higher PSNR. However, it is a comparatively minor improvement. For the stability and robustness of the experimental results, \(\alpha\) is set to the middle value 0.75. The exponents \(p_{1}\) and \(p_{2}\) in Equation (9) determine which of the two weights has a greater influence on the final weight map. As these two weights play the same role in our weight combination, we set \(p_{1}=p_{2}=1\) to consider the two weights as equally important. We carried out our experiments in MATLAB (R2018a) environment using a PC with a 4.00 GHz Intel Core i7-6700K CPU, 16 GB of RAM, and an Nvidia Quadro M4000 GPU.