# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 5 Conclusions  
In this paper, a novel two-stage denoising method supported by an unsupervised fine-tuning strategy is proposed to boost the denoising effect with the minimum time cost. Compared to the original DIP, our method takes the improved U-Transformer-generated high-quality preprocessed image as the target image, and we employ a simplified network model with only one Transformer block for fine-tuning, achieving superior denoising results with a relatively short execution time. The empirical results show that our approach exceeds both the original DIP technique and the most advanced supervised denoising methods, especially in scenarios where high-quality denoising is required. The reasons for this can be attributed to the following: (1) From a macro perspective, our proposed approach effectively integrates both supervised and unsupervised models while offering several advantages. It initially employs supervised denoising training to acquire the preprocessed image, characterized by high image quality, rendering it suitable for use as the target image. Based on this, the simplified unsupervised network can be used for fine-tuning, which significantly improves the overall denoising effect. (2) The unsupervised optimization process relies solely on a simplified network consisting of one Transformer  
Figure 10: Visual comparison of denoising effects using different methods on a real noisy image from the SIDD datatset. (**a**) Original. (**b**) Noisy. (**c**) DRUNet/29.35 dB. (**d**) SWinIR/29.61 dB. (**e**) DnCnn/29.62 dB. (**f**) BM3D/37.07 dB. (**g**) IRCNN/37.62 dB. (**h**) FFDNet/37.94 dB. (**i**) DAGL/38.00 dB. (**j**) DIP/38.68 dB. (**k**) Stage1/41.15 dB. (**l**) Stage2/42.76 dB.  
block, which greatly speeds up the entire denoising process and improves efficiency. In summary, our proposed solution offers a promising and effective approach that demonstrates significant enhancements in removing noise from both synthetic and real-world images, surpassing other mainstream denoising methods. It should be noted that the proposed unsupervised fine-tuning method exhibits excellent scalability. In future research, as new powerful supervised models are introduced, the proposed method is poised to leverage these advancements, anticipating even higher denoising performance.  
Conceptualization, [PERSON] and S.X.; methodology, S.X.; software, [PERSON] and C.Z.; validation, [PERSON], [PERSON] and S.J.; formal analysis, [PERSON], [PERSON] and S.X.; investigation, [PERSON] and S.J.; resources, [PERSON] and X.J.; data curation, [PERSON] and S.J.; writing--initial draft preparation, S.J.; review and editing, [PERSON], [PERSON] and S.X.; visualization, [PERSON] and X.J.; supervision, [PERSON] and X.J.; project administration, S.X.; funding acquisition, S.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 62162043.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflicts of interest.