# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
###### Abstract
This model enhances network training performance, expedites the process, and achieves superior results in removing additive white Gaussian noise(AWGN). However, it struggles to yield desired outcomes when the noise level is unknown. To enhance the model's generalization ability, [PERSON] et al. proposed a fast and flexible denoising network (FFDNet) [20]. FFDNet effectively and efficiently handles a wide range of noise levels by incorporating an adjustable noise level map as an input. In the latest study [26], [PERSON] et al. enhanced the denoising performance by incorporating an efficient attention module, called the convolutional block attention module (CBAM), into the FFDNet model. However, FFDNet and its variants' generalization ability can be further improved for complex noise scenarios. DRUNet [27] is a residual connection-based network designed for denoising. By combining the strengths of DnCNN and FFDNet, it employs a multi-level residual architecture to learn the intricate relationship between real signals and noise in images. Additionally, it extracts high-level feature representations through deep network structures. Compared to traditional single-level denoising networks, DRUNet excels at capturing complex noise patterns in elaborate images, resulting in more precise and reliable interference removal. However, DRUNet requires lengthy training times and requires further enhancements to address the challenges posed by extremely low signal-to-noise ratio (SNR) conditions. Furthermore, its effectiveness in suppressing noise interference within complex real-world scenes remains limited. [PERSON] et al. proposed a modular one-stage real-image denoising network (RIDNet) [28] in 2019 to enhance the practicality of denoising algorithms. RIDNet consists of three components: a feature extraction module, a feature learning residual module, and a reconstruction module. By incorporating residuals within the residual structure to alleviate low-frequency information propagation and employing feature attention mechanisms to explore channel correlations, RIDNet effectively accomplishes denoising tasks in real-world scenarios. SwinIR [29], proposed by [PERSON] et al., integrates shallow and deep feature extraction with high-quality image reconstruction. This model effectively learns the mapping relationship between low-quality images and their corresponding high-quality counterparts, restoring clear details and mitigating noise interference. Moreover, it employs a deep transformer network structure to efficiently eliminate image noise and enhance visual quality. SwinIR exhibits superior denoising performance compared to the widely adopted CNN method. However, it should be noted that its training time is relatively lengthy, and its effectiveness for very low-quality images may be limited. In summary, once the supervised denoising models are trained, their parameters are kept fixed, resulting in a constant execution time for any image processing task, thereby enhancing efficiency. However, this process relies heavily on acquiring and labeling a substantial amount of expensive labeled data. Additionally, the trained model may only demonstrate suitability for scenarios similar to the training data and might not generalize well in unknown scenarios. Researchers have addressed the poor flexibility and training difficulty of supervised denoising models by adopting unsupervised or self-supervised methods and proposing enhanced models, which offer increased adaptability and generalization ability. An example is the weakly supervised denoising model Noise2 Noise (N2N) proposed by [PERSON] et al., utilizing pairs of noisy images from the same scene for training without requiring ground truth images, achieving noise reduction performance comparable to supervised models [30]. However, its applicability is limited by the need for a large number of diverse noisy images from the same scene. Nevertheless, the statistical plausibility of using noisy images as supervision has been demonstrated, inspiring subsequent research.