**6. Conclusions**

In this paper, we demonstrated the effect and performance of a kernel prediction network and a deep generative adversarial network to construct an end-to-end general denoising network structure with loss function, and the comparative experiments reflected that our proposed method was effective and had a good denoising effect. In addition, our results were compared with the recent work results of Monte Carlo-rendered image denoising. Accordingly, the comparison results showed that both the visual effects of the image, the measured PSNR, and SSIM showed that our approach had a grea<sup>t</sup> improvement against the state-of-the-art. In addition, the denoising effects of the data rendered by multiple renderers showed that the network model of this paper had a relatively good generalization ability and a good adaptability to the rendering data from different rendering systems.

In contrast, to analyze the performance of the method proposed in this paper, we inputted noise images with different sampling rates and compared the denoising effect and running time. The results showed that the method of our approach method achieved better results in terms of effect and running time.

**Author Contributions:** Conceptualization, A.M.T.A. and C.C.; methodology, A.M.T.A.; software, A.M.T.A.; validation, A.M.T.A.; formal analysis, A.M.T.A.; investigation, A.M.T.A.; resources, C.C.; data curation, A.M.T.A.; writing—original draft preparation, A.M.T.A.; writing—review and editing, A.M.T.A. and C.C.; visualization, A.M.T.A.; supervision, C.C.; project administration, C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported partially by the National Natural Science Foundation of China under Grant U19A2063 and partially by the Jilin Provincial Science & Technology Development Program of China under Grant 20190302113GX. The authors would like to thank all reviewers for their valuable comments and suggestions.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.
