# Boosting of Denoising Effect with Fusion Strategy
## 4 Experiments
### Visual Comparison with Other State-of-the-Art Algorithms  
As the ultimate judges of image quality are human subjects, visual quality is also critical in evaluating a denoising method. Therefore, we focus on the visual comparison of the images denoised by the eight competing methods in this study. The results of the experiment at the noise level \(\sigma=20\) for the test image _Boat_, shown in Figure 6, illustrate that the proposed method can preserve the contrast and structural details almost entirely. Comparing our method with other methods, it can be observed that the results of the NCSR and PGPD have lost several image details, whereas BM3D, WNNM, DnCNN, and TWSC produced over-smoothed results in the highlighted red window. Furthermore, FFDNet tends to generate several artifacts on the sign of the boat, where the proposed method obtains a smooth result. Particularly, the proposed method can recover well the thin masts of the boat. These masts are almost absent in the recovered images obtained by other methods. Subsequently, we increased the noise level to 50. It can be observed from Figure 7 that PGPD, BM3D, NCSR, and FFDNet tend to smooth the edges and textures, which leads to image blurring. Although DnCNN, TWSC, and WNNM better balance the contrast, they generate substantial artifacts on the flower in the image _Monarch_. In contrast, the proposed method can well reconstruct the vein-like patterns in the butterfly's wing that are shown in the magnified view; furthermore, the proposed method better preserves the edge structures of the test image. Overall, the proposed method produces denoised images of the best visual quality while maintaining high PSNR indices. In addition, we test our method on the BSD dataset. It is clear from the results that the proposed method exhibits a visual performance that is superior to that of the other denoising methods. Visual comparisons of the results obtained using the various denoising methods are shown in Figure 8. It can be seen that NCSR generates substantial artifacts between the zebra's stripes, and DnCNN balances the contrast well; nevertheless, it tends to distort the lines and generate blurred edges. It is not surprising that our method can preserve much more sharp edges and fine details because it is a combination of NCSR and DnCNN via the proposed fusion strategy, which is highly promising. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Comparison Method** & **NCSR** & **WNNM** & **PGPD** & **BM3D** & **DnCNN** & **TWSC** & **FFDNet** \\ \hline \(p\)-value & 0.000000 & 0.000018 & 0.000000 & 0.000000 & 0.000003 & 0.000001 & 0.003448 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multiple comparisons for Two-Way ANOVA of the average PSNRs (dB) of the commonly used test images. For visual comparison, Figure 9 shows the denoised images, corresponding to an image in the ESPL synthetic image database, that were obtained using the various methods evaluated in this study. A magnified view is also provided for each image for better visual comparison. It can be seen that a number of noise pixels have not been removed in the images denoised by NCSR, PGPD, BM3D, and TWSC; moreover, details have been extensively lost in the lower right corner of the image. Regarding the denoised image obtained using FFDNet, many undesirable bright pixels are generated on the wings of the cartoon girl. Furthermore, WNNM and DnCNN produce over-smoothed textures and edges. By comparison, the result obtained using the proposed method retains the information in the image image. Figure 6: Comparison of denoising results of the competing methods for image _Boat_ with noise level \(\sigma=20\). (**a**) Original image. (**b**) Noisy image. (**c**) NCSR, PSNR = 31.02 dB. (**d**) WNNM, PSNR = 31.31 dB. (**e**) PGPD, PSNR = 31.06 dB. (**f**) BM3D, PSNR = 31.21 dB. (**g**) DnCNN, PSNR = 31.44 dB. (**h**) TWSC, PSNR = 31.29 dB. (**i**) FFDNet, PSNR = 31.43 dB. (**j**) Proposed, PSNR = **31.76** dB.