# Boosting of Denoising Effect with Fusion Strategy
## 1 Introduction
Taking advantage of batch normalization [28] and residual learning [29], the DnCNN can handle several prevailing denoising tasks with high efficiency and performance. Various image denoising algorithms have produced highly promising results; however, the experimental results and bound calculations in [30] showed that there is still room for improvement for a wide range of denoising tasks. Some image patches inherently require external denoising; however, external image patch prior-based methods do not make good use of the internal self-similarity. Further improvement of the existing methods or the development of a more effective one using a single denoising model remains a valid challenge. Therefore, we are interested in combining both internal and external information to achieve better denoising results. To this end, we choose NCSR and DnCNN as the initial denoisers by considering their performance and complementary strengths. NCSR, a powerful internal denoising method that combines nonlocal similarity and sparse representation, demonstrates exceptionally high performance in terms of denoising regular and repeated images. The DnCNN possesses an external prior modeling capacity with a deep architecture. This is better for denoising irregular and smooth regions and is complementary to the internal prior employed by the NCSR. In other words, the combination of NCSR and DnCNN can strongly explore both the internal and external information of a given region in the initially denoised images. In this study, we introduce a denoising effect boosting method based on an image fusion strategy. The objective is to further improve performance by fusing images that are originally denoised by NCSR and DnCNN. These methods have complementary strengths and can be chosen to represent the internal and external denoising methods. Note that, the proposed denoising effect boosting method is simpler than the deep learning-based one introduced in [31]. In the latter method, a CNN is leveraged to iteratively learn the denoising model in each stage in the deep boosting method; this requires massive images for training to achieve an appropriate final result. In contrast, our method boosts the denoising effect using the image fusion strategy. Without using any training samples, we compute the weight map along each image pixel to fuse two initially denoised images for an enhanced denoising effect. In summary, the novelty of our method lies in three aspects. First, our method combines complementary information from images denoised using two state-of-the-art methods via a fusion strategy. Second, the strategy is excellent in terms of the preservation of details via a simple fusion structure. Third, it does not involve a computationally expensive training step. The DnCNN model used in this study was trained by its original developers, and the parameters are set using the source code of the model. Furthermore, NCSR is based on the nonlocal self-similarity and sparse representation of image patches, which need not be learned from external samples. Therefore, our method does not involve any loop iterations for processing images. The effectiveness of the proposed denoising booster can be seen in Figure 1, where some test images and the corresponding denoisedimages are shown. The proposed booster performs well with regard to the preservation of the image details. In the _Lena_ image, the NCSR can recover the eyelashes; however, it produces artifacts on the eyeball. Though DnCNN produces less artifacts, it tends to create an over-smooth region, with the eyelashes being almost invisible. However, by combining the strengths of these two methods, our method can preserve more details without generating many artifacts in the same region. We can also observe that the line in the _House_ image has a gray intensity in the result obtained using the NCSR. Nevertheless, it becomes brighter after boosting is performed by combining the denoising performance of the DnCNN with that of the NCSR.