# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 4 Experiments
### Visual Comparisons  
In image processing, visual quality is an important criterion for evaluating denoising effects [42]. To intuitively perceive the visual quality, we present the comparison results of images with rich texture information. Figure 7 visually shows the denoising results of the Couple image from common10 with a noise level of 30 using different denoising methods. We chose the portion of the Couple image with curtains, which was enlarged in the lower right corner of each image for a detailed comparison. It can be observed that BM3D is not effective since a lot of detail is lost in the location indicated by the red arrow on the right, and, compared to the original image, the portion of the curtain texture indicated by the yellow arrow is also distorted. NCSR cannot preserve the details of the curtain folds and its denoised image is severely distorted, especially in the right part. For the denoised image by VDNet, the blurry spots from the noise cannot be eliminated effectively, resulting in an undesirable result. CsNet and WNNM produce smooth edges; however, the texture details are not adequately preserved. While FFDNet and DnCNN preserve more texture details, they are more likely to produce over-smoothened artifacts. In the case of the proposed method, we observed from the enlarged part of the image that the details of the curtain folds were improved, and the discernibility of the curtain edge was enhanced, which is much closer to the original image. Overall, our method yielded a satisfactory denoised image with better visual effects and increased the PSNR value to 29.43 dB when compared with the aforementioned seven methods. Figure 7: \(Cont.\)The visual results of the typical methods on the Hill image are shown in Figure 8. We magnified the roof part of the image to compare the visual differences in detail. BM3D cannot effectively handle the boundary part of the roof, making the edges blurred. The denoising results of FFDNet and VDNet, which are representatives of the learning-based methods, are too smooth and completely lose the details of the roof section. The NCSR and DnCNN preserve a small portion of the roof texture but it is still not apparent. Moreover, compared to the original image, there are distortions and artifacts, indicated by the arrows. As a representative of the image combination strategy, CsNet does not show superior results in this region compared to other stand-alone denoisers. This is because the granularity of its image combination strategy is too large, making it difficult to obtain optimal denoised results in some regions of the images. The performance in the magnified region of our method is superior to that of the six above methods. Specifically, our method achieves competitive results in terms of the edges of the roof details, and the textural regions are effectively retained, preserving information in the original image to the greatest extent. Since our denoising method is extensible, this allows us to easily extend the denoising method to color image denoising. Therefore, we conducted experiments with color versions of the common dataset as well. Figure 8: _Cont._  
Figure 7: Denoising results of Couple image with noise level \(\sigma=30\). For color image denoising, we chose a color version of the Boat image and the results are shown in Figure 9. We magnified the roof part of the image to compare the visual differences in detail. Since the typical BM3D method can only process grayscale images, we chose its variant, CBM3D, to conduct the visual comparison experiments on color images. In the denoised image of FFDNet, we observed that the detail part of the white line on the boat, indicated by the yellow arrow, was preserved well, but the lower right corner of the magnified area, indicated by the red arrow, showed a serious distortion, where many details were ignored. In contrast, CBM3D processed the image well at the white line indicated by the red arrow but failed to preserve the details indicated by the yellow arrow.