## 1 Introduction
Subsequently, the self-supervised denoising models Noise2 Void (N2V) [11] and Noise2 Self (N2S) [12] proposed by [PERSON] et al. and [PERSON] et al. can train the network with only one noisy observation for each scene. The N2V model uses a blind spot training strategy, in which the model excludes or masks pixels in the center of its receptive field during training. This design is based on the assumption that the noise has zero mean and is independent at the pixel level. By analyzing the local image context (not including the aforementioned blind spots), the model can predict the true intensity of a pixel. This strategy builds a probabilistic model through N2V to achieve coverage of noisy observations and pixel-level real signals. In this way, the N2V model effectively handles various noise levels and can effectively remove spatial variability noise. N2S utilizes a blind spot network architecture and a self-learning approach to predict a pixel's value by examining its surrounding pixels. Again, regardless of the \"blind spot\", the network implicitly learns the statistical properties of the noise present in the image, which allows the model to distinguish between the noise in each pixel and the underlying signal. This self-supervised approach allows the network to estimate the denoiser from noisy data, making it more effective against random noise mitigation. In research since then, probabilistic Noise2 Void (PN2V) [13] and dilated blind-spot network [14] have introduced explicit noise modeling and probabilistic inference), as well as mask convolution and stacked expanded convolutional layers for better performance and faster training. However, the noise model is difficult to specify, especially in realistic scenarios of ultrasound images. [PERSON] et al. [15] proposed a self-supervised framework called Neighbor2 Neighbor, which trains a denoiser primarily by performing a single observation on noisy images. The framework mainly consists of two parts. First, a pair of noisy images is generated by random adjacent sub-samplers. Then, these sub-sampled image pairs are utilized for self-supervised training, while regularization loss is also introduced to cope with the non-zero actual difference between pairs of sub-sampled noise images. The regularization term mainly consists of the reconstruction part and the regularization part. Building upon the Neighbor2 Neighbor framework, [PERSON] et al. (2016) have integrated high-resolution anatomical MR images as auxiliary information, proposing a self-supervised method named neighbor-to-neighbor (NB2 NB) that denoises PET images using a single noisy input. By employing a U-Net network architecture across three different resolution levels, this method can more delicately capture and restore the details in PET images, thereby significantly enhancing the denoising effect. This multi-resolution strategy not only optimizes noise suppression but also ensures the quantitative accuracy of the images, making them more suitable for specific types of medical imaging data. Due to the limitation of independent noise, the current unsupervised denoising methods such as Ne2 Ne cannot deal with correlated noise in ultrasound images. Meanwhile, the model causes the loss of pixels due to the random neighborhood downsampler adopted by the model. To enhance the generalization ability and detail recovery capacity of the denoising model, our work proposes a new self-supervised learning ultrasound image denoising framework according to the limitations of the Ne2 Ne algorithm. The main contributions of this paper are as follows:  
(1) Based on the Ne2 Ne algorithm, a term is added to constrain the structural similarity between neighborhood images, to suppress independent and correlated noise more effectively. (2) Meanwhile, to enhance the quality of the denoising effect of the model, the downsampling strategy is differentially reorganized and optimized to ensure that the sub-image details of the subsamples are preserved.