# An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers
###### Abstract
Then, by using collaborative filtering of the generated estimate, the noise reduction effect was increased. However, the block-wise process may result in distorted output. For instance, when the noise level rises gradually, the denoising performance of the model decreases, and artifacts emerge, particularly in flat regions. In 2011, [PERSON] et al. presented a non-locally centralized sparse representation (NCSR) [14]. It makes use of a sparse representation of image patches and non-local similarity, altering the goal of image denoising to suppressing sparse coding noise. The NCSR has demonstrated remarkable performance for natural images with both smooth and textured regions. However, the algorithm is not suitable for many applications owing to its computational complexity in non-local estimates of unknown sparse coefficients. Although these model-based methods have fundamental mathematical derivations, they still have a few drawbacks. First, they are frequently time-consuming owing to the complexity of the iterative optimization processes. Second, only the internal information of the noisy image is used by the model-based methods. No external information is used; this leads to the denoising effect being considerably impaired while recovering flat regions and texture patterns with excessive noise [15]. With the rapid development of deep learning, learning-based methods have shown great potential in image denoising [16]. The learning-based methods aim to train a deep denoising network with datasets and learn the noise model implicit mapping from the noisy to the clean images to form external priors [2]. In recent years, the popularity of learning-based methods has surpassed that of model-based methods. In 2016, [PERSON] et al. suggested a feed-forward denoising convolutional neural network (DnCNN) [17]. They incorporated residual learning [18] and batch normalization [19] to speed up the training process while improving denoising outcomes. With the supervision of noisy-clean paired images, it achieved superior performance compared to traditional methods. However, the model can only handle noisy images well when the noise level is in the preset range; hence, it lacks generalization capability. To alleviate the problem in DnCNN of a lack of flexibility, [PERSON] et al. introduced a flexible and fast CNN solution for CNN-based image denoising called FFDNet [20]. It contains an adjustable noise level map and can be trained to recognize the noise level. Experiments revealed that the method can handle several images with different noise levels, with high performance and efficiency. However, the denoising performance of FFDNet is limited to the training dataset, indicating that it can only obtain excellent results on noisy images that are similar to the images of the training set. In other words, FFDNet may generalize poor results for input images that are corrupted with complex noise, especially real-world noisy images. To improve the generalization capability of the deep CNN denoiser, in 2019, [PERSON] et al. used real-synthetic noise and real-image noise to train a convolutional blind denoising network (CBDNet) [21]. Although CBDNet performed well under different noise levels, it is less efficient and flexible owing to its separate subnets and the need for manual intervention. Soon after, [PERSON] et al. presented a variational denoising network (VDNet) [22]. It automatically estimates the noise distribution and the noise-free image inside a unique Bayesian framework using variational inference methods. The VDNet surpassed CBDNet in both performance and generalization capability. However, it does not consider signal autocorrelation, making it difficult to estimate the real-world noise in some forms. In 2022, for the enhanced control and interpretability of a learning-based denoiser, [PERSON] et al. proposed a novel architecture based on a denoising network called controllable confidence-based image denoising (CCID) [23]. CCID merges the deep denoising network with a reliable filter to reduce artifacts.