# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
## 5 Conclusions  
In this study, a weight map generative network was proposed. Rather than designing a novel denoising model from scratch, we combined the initial denoised images processed by different typical and effective denoisers at the pixel level to produce the optimal denoised images. Specifically, for the experiment, we found that the combination of BM3D and FFDNet has the best performance. For BM3D, we used the non-local self-similarity such that the internal information of the target image can be fully mined, alleviating the excessive reliance on the dataset. For FFDNet, we utilized its deep learning network to construct an external prior. After the two models completed the denoising process for the noisy image separately, the superior part of the two results was combined to generate an optimal denoising result via corresponding weight maps. We used an unsupervised deep learning approach to generate the weight map, as manually setting a specific weight value for each pixel point of an image is complex and difficult. This method is called the pixel-level image combination strategy. The experimental results demonstrate that our method presents a significant overall improvement compared to the results of other state-of-the-art methods, surpassing the second-best method by 0.03 dB to 1.42 dB on average, on two datasets with different noise levels. In particular, our method increased the PSNR results by an average of 0.69 dB compared to BM3D, 0.48 dB compared to FFDNet and 0.34 dB compared to CsNet. The method also performed well in visual comparisons in terms of image details and textures, in processing both common and color test images. It should be noted that our method is an extensible image combination strategy, which is reflected in two aspects. Our method is flexible in the selection of the type and number of initial denoising methods. As new denoising methods have been continuously proposed, future studies can select more efficient complementary methods to further increase the performance in terms of execution efficiency and denoising effect. Our method is based on a general image combination strategy, indicating that it is not only limited to image denoising. It would be reasonable to extend our image combination strategy to other image processing problems, such as image deblurring or low-light image enhancement. Figure 10: Enhancement effect of each method on the typical low-light image selected from [48]. Generally speaking, the metrics for evaluating denoising results are mainly in two aspects: effect and efficiency. For every new denoising method, the most important goal is to improve the denoising effect. In this work, we focus on a combination of stand-alone methods to improve the denoising effect; therefore, the processing time is inevitably longer than that of stand-alone denoising methods, and investigating how to reduce the processing time was beyond the scope of this study. In future research, we will explore methods to reduce the processing time while maintaining an excellent denoising effect. Specifically, we may achieve this in the following ways: with the development of denoising techniques, a natural but fundamental idea is to find some more efficient stand-alone denoising methods to reduce the pre-processing time; moreover, we can also improve the efficiency of our method by optimizing our weight map generative network. At present, there are two general implementations of combination networks, unsupervised and supervised. Our network belongs to the unsupervised approach, which allows its network parameters to be adapted to any given initial denoised images without requiring training pairs. This image-specific denoising network allows us to produce optimal combined images but with a longer processing time. Although supervised networks have advantages in terms of testing time, they require a large number of labeled training images, which are expensive to collect.