# Boosting of Denoising Effect with Fusion Strategy
## 1 Introduction
The boosting process is formulated as an adaptive weight-based image fusion problem to enhance the contrast and preserve the image details of the initially denoised images. Specifically, unlike many existing conventional pixel-wise image fusion methods that employ one weight to reflect the pixel value in the image sequence, our method applies a weight map to adaptively reflect the relative pixel intensity and the global gradient of the initially denoised images obtained using the NCSR and the DnCNN, respectively. Taking the overall brightness and neighboring pixels into consideration, two kinds of weights are designed as follows:  
1. The relative pixel intensity based weight is designed to reflect the importance of the processed pixel value relative to the neighboring pixel intensity and the overall brightness. 2. The global gradient based weight is designed to reflect the importance of the regions with largely variational pixel values and to suppress the saturated pixels in the initial denoised images. A linear combination of these two kinds of weights determines the final weight. Two initially denoised images are incorporated into the fusion framework, and the boosting method can significantly combine the complementary strengths of the two aforementioned methods to achieve better denoising results. Several extensive experimental results demonstrate that the proposed method visually and quantitatively outperforms many other state-of-the-art denoising methods. The key contributions of this study are summarized as follows:  
Figure 1: Examples showing the effectiveness of the proposed booster in improving NCSR and DnCNN. (**a**) Original image. (**b**) Noisy image. (**c**) Input image denoised by NCSR, PSNR = 29.11 dB. (**d**) Input image denoised by DnCNN, PSNR = 29.50 dB. (**e**) Our final denoised result, PSNR = 30.04 dB. (**f**) Original image. (**g**) Corrupted image. (**h**) Input image denoised by NCSR, PSNR = 32.13 dB. (**i**) Input image denoised by DnCNN, PSNR = 32.26 dB. (**j**) Our final denoised result, PSNR = 33.29 dB. * Optimal combination. We introduce a denoising effect boosting method to improve the denoising performance of a single method, NCSR or DnCNN. Each denoiser has its own characteristics. The NCSR performs well on images with abundant texture regions and repeated patterns. Owing to the strategies of residual learning [29] and batch normalization [28], the DnCNN is better for denoising irregular and smooth regions. A linear combination of NCSR and DnCNN is better than either of the individual methods as well as a number of other state-of-the-art denoising methods. To the best of our knowledge, the proposed denoising effect boosting method is the first of its kind in image denoising. * Weight design. We introduce two adaptive weights to reflect the relative pixel intensity and global gradient. One is to emphasize the processed pixel value according to the surrounding pixel intensities and the overall brightness. The other is to emphasize the areas where pixel values vary significantly and to suppress saturated pixels in the initial denoised images. Therefore, the weight design is powerful in preserving image details and enhancing the contrast when denoising. In Section 2, we first review two denoising methods, NCSR and DnCNN, and highlight their contributions to our study. In Section 3, we describe the proposed method in detail and present the proposed adaptive combination algorithm. In Section 4, the experimental results obtained using the proposed method are compared with those of other state-of-the-art methods. In Section 5, we discuss the results in detail. Finally, we conclude the study and discuss the directions for future research in Section 6.