## 1 Introduction  
Ultrasound imaging technology is widely used in the medical field because of its non-invasiveness, low cost, high efficiency, convenience, and real-time performance. It has become one of the core tools for diagnosing various diseases in hospitals, especially in monitoring fetal development in pregnant women and diagnosing abdominal organ diseases. However, due to the special mechanism of ultrasound imaging, speckle noise is frequently generated during the imaging process, which reduces the quality of ultrasound images, mainly manifesting in poor image contrast and inconspicuous features characterizing the structural properties of tissues. These problems not only affect the clarity of the image but may also affect the accuracy of the diagnosis. Therefore, it is of vital significance to promote the quality of ultrasound medical images and provide auxiliary support in disease diagnosis. In medical ultrasound imaging, low-frequency (3-30 MHz) ultrasound pulses are delivered to the patient through a sensor [1]. These pulses are reflected at the interface of different tissues and are captured by sensors and converted into electrical signals to generate ultrasound images. However, speckle noise frequently appears during this conversion, which is random interference resulting from scattering by fine particles [2]. This noise causes the image to appear as granular patterns in the bright and dark regions due to the random phase change and destructive interference of the ultrasound pulse. The existence of speckle noise reduces the detail resolution of the ultrasound image and then affects the overall quality of the image and the accuracy of the diagnosis. For the modeling of an echo envelope, Rayleigh distribution is commonly used to describe the echo envelope signal in ultrasound imaging [3]. In ultrasound image processing, common methods used to reduce speckle noise include the multi-angle plane composite technique and post-image filtering. Composite techniques include methods for combining content of different frequencies or images from different spatial views [4; 5]. The post-image filtering method has been widely used in B-mode imaging [6]. These methods assume that speckle is a multiplicative noise, which can be filtered out, thus achieving the effect of suppressing speckle noise and ultimately raising the quality of the image. Among them, the nonlinear filter has the characteristics of retaining edges and smooth uniform areas [7]. For the past few years, with the development of deep learning, extensive researchers have used various convolutional neural networks to process image tasks. Furthermore, ultrasound image denoising is also one of the hotspots explored. [PERSON]. [8; 9] achieved the goal of image denoising by adding residual terms to the CNN network and formed a flexible image denoising solution. The above deep learning methods work extremely well in Gaussian denoising. However, these algorithms are based on supervised learning and require more noisy-clean image training. This kind of fully supervised learning method has a limited denoising effect in the practical application of ultrasound images. In contrast, unsupervised learning methods that do not rely on noise-free images appear to be more applicable for speckle denoising in ultrasound images. [PERSON] et al. [10] proposed a depth noise canceller based on Noise2 Noise (N2N) to train multiple noises with the same scene and learn the mapping relationship between two zero-mean noise images. When the number of samples is tiny, the transformation relationship of two zero mean noise patterns is learned. When the number of samples is enormous enough, since the noise is unpredictable, from the perspective of minimizing the loss function, N2N tends to output the expectation of all possible outputs, which is the signal itself of the clean image. However, N2N requires two noisy observations of the same scene, and this condition is also difficult to satisfy in realistic scenarios.