# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
###### Abstract
Subsequent models, such as Noise2 Void and Noise2 Self, were introduced to train deep neural networks using only unorganized noisy image pairs [31; 32]. Noise2 Void avoids learning identity mapping by employing blind-spot networks that utilize neighboring pixels to predict each pixel. Similar strategies are employed in the parallel work of Noise2 Self. Despite achieving good denoising results, these models use noisy images for training that are heavily correlated with the observed noisy image, posing challenges and costs in practical collection. In contrast to blind-spot methods, Noisier2 Noise and Noisy-as-Clean generate training pairs by adding synthetic noise to the observed noisy image [33; 34]. The Noise-As-Clean (NAC) method introduces additional noise to the original noisy image, creating noise-noise pairs for training deep neural networks. By employing the NAC strategy, an existing supervised noise reduction model can seamlessly transition into an unsupervised model through a simple modification of the training pair. However, accurately determining a reliable noise model remains challenging in real-world scenarios. To enhance denoising performance, Neighbor2 Neighbor constructs a noisy pair by subsampling a given noisy image [35]. However, its performance still relies heavily on a large-scale training dataset. Recently, there has been a focus on addressing the challenges posed by noisy images, with some pioneering works highlighting the significance of incorporating information from both noisy and neighboring pixels during image recovery. However, these methods are often highly sensitive to the standard deviation of the noise added to clean images, a parameter that is typically unknown without access to the clean images themselves. To mitigate this challenge, Noise2 Info, introduced by [PERSON] et al. in 2023 [36], was proposed. This method aims to extract critical information, specifically the standard deviation of the noise solely based on the characteristics of the noisy images. In contrast to methods requiring numerous noisy images, Self2 Self [37], introduced by [PERSON] et al., utilizes a single noisy image without ground truth for training, applying Bernoulli sampling to generate a sequence of noisy images for network training. Quadratic regularization of sampled pixels is performed through local convolution, enhancing network performance. Nevertheless, diminishing reliance on a single image for training may lead to overfitting issues. Zero-shot Noise2 Noise (ZS-N2N), as proposed by [PERSON] and [PERSON] in 2023 [38], represents a novel approach to image denoising. This method is designed to address scenarios where data availability is scarce and limited computing resources are available. Unlike traditional denoising methods that require pairs of clean and noisy images for training, ZS-N2N trains a lightweight network using only a single noisy image. The training target is derived from downsampled image pairs, allowing the network to learn denoising capabilities efficiently. Similarly, the deep image prior (DIP) method was proposed to train solely on a single degraded image, leveraging structural priors embedded within the network architecture itself [39]. However, its sensitivity towards target images requires further enhancements to improve performance. In summary, unsupervised and self-supervised denoising models still exhibit a significant performance gap compared to supervised models due to challengesin obtaining high-quality training data and their relatively slow training process. Moreover, they are more sensitive to initial conditions and hyper-parameters. Improving the noise reduction effect has always been the primary goal pursued by researchers. Although many of the supervised methods now proposed have good performance, they are limited by the specific dataset and cannot achieve good results in various scenarios. Meanwhile, the unsupervised method has a wide range of adaptivity, which can compensate for the shortcomings of the supervised denoising process.