# Boosting of Denoising Effect with Fusion Strategy
## 5 Discussion  
There are two important indicators of denoising performance: the denoising effect and computational complexity. Unfortunately, a high denoising performance is often obtained at the cost of computational complexity; therefore, the development of denoising methods is a spiraling process. The current denoising models must seek a reasonable trade-off between denoising performance and run time. This encourages researchers to continue to focus attention on improving the current state-of-the-art models. The computation time of our method comprises the fusion time of the initially  
Figure 8: Comparison of denoising results of the competing methods for the test image with noise level \(\sigma=40\). (**a**) Original image. (**b**) Noisy image. (**c**) NCSR, PSNR = 26.73 dB. (**d**) WNNM, PSNR = 27.46 dB. (**e**) PGPD, PSNR = 27.03 dB. (**f**) BM3D, PSNR = 26.55 dB. (**g**) DrCNN, PSNR = 27.85 dB. (**h**) TWSC, PSNR = 27.44 dB. (**i**) FFDNet, PSNR = 27.82 dB. (**j**) Proposed, PSNR = 28.35 dB. Figure 9: Comparison of denoising results of the competing methods for image selected from the ESPL synthetic image database with noise level \(\sigma=50\). (**a**) Original image. (**b**) Noisy image. (**c**) NCSR, PSNR = 29.21 dB. (**d**) WNNM, PSNR = 29.76 dB. (**e**) PGPD, PSNR = 29.56 dB. (**f**) BM3D, PSNR = 29.39 dB. (**g**) DrCNN, PSNR = 29.94 dB. (**h**) TWSC, PSNR = 29.59 dB. (**i**) FFDNet, PSNR = 30.32 dB. (**j**) Proposed, PSNR = 30.62 dB. denoised images and the running time of NCSR and DnCNN; therefore, it is longer than the running time of the single denoiser. However, unlike several deep learning-based boosting methods, the fusion step in the proposed method does not involve the training stage, which is time-consuming. The fusion times of our method for processing six images selected from the ten commonly used test images employed in this study, with sizes of \(256\times 256\) and \(512\times 512\), are listed in Table 9. We evaluate the fusion time by denoising the ten images with noise levels of 10, 30, and 60. It can be seen that the fusion process takes very little time; therefore, the computational complexity mainly depends on the two algorithms to be fused. Our goal is to introduce a novel method for boosting the denoising effect using an image fusion strategy. With the evolution of the denoising methods to be fused, the efficiency of our method will increase. The proposed method allows the combination of the initial denoised images generated by any two image denoisers; thus, one can train two complementary algorithms that are different from the ones employed in this study and use our method to boost the denoising effect. In summary, the proposed method achieves optimal results at a reasonable computational cost; furthermore, it allows for an effective performance/complexity trade-off in the future. Whereas image denoising algorithms have produced highly promising results over the past decade, it is worth mentioning that it has become increasingly difficult for several denoising methods to achieve even minor performance improvements. According to [PERSON] et al. [49], when compared over the BSD dataset, for \(\sigma=50\), the predicted maximal possible improvement (over the performance of BM3D) for external denoising tasks is bounded by 0.7 dB. However, the proposed method exceeds the performance of BM3D by 0.77 dB, as shown in Table 5, which is a substantial improvement. Through the image fusion strategy, our method offers a solution to further improve individual internal or external denoising algorithms. The fused image can provide a visually better output image that contains more information. Therefore, it is worth achieving a more specific and accurate result using our method at the reasonable computational cost. In fact, there are abundant real-world applications (e.g., machine vision, remote sensing, and medical diagnoses) that can benefit from the proposed method.