# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
###### Abstract
The results revealed that CCID improves the interpretability and control and outperforms the former deep denoisers, such as DnCNN, especially when the test data differ from the training data. Compared to the model-based denoising methods, learning-based methods are advantageous in the following two aspects. (1) They benefit from numerous datasets. These learning-based methods make full use of external information and demonstrate an extremely competitive denoising effect. (2) Although the learning-based methods need to utilize their network to extract features, which is time-consuming, the inference of CNN is very efficient at the test stage owing to the computation ability of the GPU. Extensive experiments demonstrate that their processing speed is mostly higher than that of model-based methods [24]. However, their strong dependence on data results in certain drawbacks. Owing to their strong dependence on external information, the performance of learning-based methods is limited to their training data [25]. These methods find it difficult to deal with universal image denoising problems because they are not adaptive to a given noisy image with different noise levels, image texture and scene classification from the dataset; some fine-scale image details may not be well preserved. Therefore, for the learning-based models, their strong data dependence tends to result in poor generalization capability and flexibility [26]. Although various image denoising methods have been proposed and have achieved highly promising results over the last decade, none of the denoising methods is perfect owing to their technical characteristics. We can safely state that it is difficult for an individual denoiser to promote the denoising performance better than others under various conditions. Conversely, model-based methods and learning-based methods are complementary to each other. For example, model-based methods are adaptable to strong self-similarity images [27], whereas the learning-based methods need cumbersome training to learn a model before the test stage and tend to be restricted by certain images. As a result, various strategies have been proposed to investigate a combination of these methods for denoising, leveraging their respective advantages. To investigate their integration, which uses their respective merits, [PERSON] et al. [28] considered a combination of the denoised images obtained using these methods and then proposed a Consensus Neural Network (CsNet). However, there is still scope for further improvement. For example, it ignores the fact that each initial denoiser contains denoising characteristics regarding the details of local areas. Therefore, it is unsuitable to estimate the weight for each initial denoised image. Therefore, this study aims to further enhance the denoising effect. We propose a weight map generative network for the pixel-level image combination of initial denoised images to obtain an optimal, well-combined image. The contributions of this study are as follows:  
1. Compared to CsNet [28], which sets a stand-alone weight for each initial denoised image, we introduce a finer combining granularity scale in this study. Specifically, we use a deep learning network to set appropriate weights for each pixel, which contain information about the contribution of the initial denoised images on the combined image, ensuring that it can retain the details well. We call this a pixel-level combination strategy. 2. Conventional supervised CNN-based denoising methods need noisy-clean paired images to achieve high performance. In our study, we use an unsupervised learning method to achieve the optimal weight maps without noisy-clean paired images [29]. It reduces the cost of collecting considerable training pairs for training and makes the generalization capability of our method excellent. 3. The proposed method can be easily extended, allowing the free combination of several initial denoised images generated by any denoiser.