## 1 Introduction  
Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technology [1] that provides real-time imaging without using radiation. It has demonstrated significant potential in various medical fields, including tumor detection, cardiovascular disease, and ophthalmology [2], and has become an indispensable tool for both clinical diagnosis and scientific research. The technology operates based on the principle of low-coherence interference, generating micron-level cross-sectional images that allow for the precise capture of microstructural alterations in ocular tissues. However, the scattering effect of light in biological tissues, particularly when reflected from rough surfaces or internal inhomogeneous media, produces randomly distributed interference patterns, known as speckle noise. This noise inevitably superimposes on OCT images, leading to the blurring of fine structures and key details of the retina [3]. This presents a significant challenge for the clinical observation and accurate diagnosis of retinal lesions. Therefore,ensuring the generation of high-quality image outputs is critical for OCT applications to accurately identify and assess subtle lesion areas. The current approaches for processing speckle noise in OCT images primarily includes hardware-based and software-based methods. Hardware-based denoising methods encompass multi-fiber design [4], multi-frequency synthesis [5], multi-channel optical range difference synthesis [6], and polarization diversity detection [7], etc. However, these methods often require complex design and manufacturing processes, which block their widespread adoption in clinical applications. Software-based denoising methods primarily include traditional denoising methods and deep learning methods. Traditional denoising methods encompass methods such as generalized Bayes [8], non-local mean denoising [9], and 3D block matching [10]. Compared to hardware-based methods, traditional software denoising methods offer the advantage of adaptability to various OCT systems and imaging requirements through targeted algorithm optimization. However, their effectiveness is often limited by their ability to retain image details. As a result, the denoised images exhibit suboptimal quality, with significant structural information being lost, which hampers the achievement of high-quality OCT images required for clinical applications. In recent years, deep learning-based methods, particularly convolutional neural networks (CNNs) [11], have exhibited excellent performance in medical image denoising, offering novel and effective solutions for generating high-quality, efficient images. Deep learning-based methods are broadly categorized into unsupervised, semi-supervised, and supervised modes [12], depending on the extent of labeling in the training data. For instance, [PERSON] et al. applied the N2N strategy to retinal OCT image denoising, which requires only noisy datasets, using U-Net as the denoising network to achieve significant noise reduction [13]. [PERSON] et al. employed a super-resolution architecture based on residual networks combined with the N2N strategy to enable real-time noise reduction in OCT images across multiple samples [14]. [PERSON] et al. proposed denoising diffusion probabilistic models (DDPMs), which realized the automatic generation of photorealistic OCT images of the retina by inputting an initial retinal layer [15]. [PERSON] et al. proposed a one-step enhancer (OSE) for simultaneous OCT denoising and deblurring via unsupervised learning [16]. However, unsupervised methods face limitations in improving performance metrics such as PSNR and SSIM, especially in handling complex noises or preserving fine structures, and they often perform worse than supervised methods. To generate superior-quality retinal OCT images, [PERSON] et al. proposed a method that constructs a feed-forward denoising convolutional neural network (DnCNN) integrating a deep architecture designed for image denoising [17]. [PERSON] et al.