## 1 Introduction
introduced a simultaneous denoising and super-resolution OCT image processing method based on generative adversarial networks (SDSR-OCT), aimed at reconstructing OCT images at varying resolutions [18]. [PERSON] et al. proposed a dual denoising network (DudeNet), which employs a dual network architecture to extract diverse features, enhancing features representation [19]. [PERSON] et al. introduced a multi-scale denoising generative adversarial network (MDGAN), based on a cascaded multiscale module and incorporated a spatial attention mechanism to further optimize the denoised image [20]. The next year, the same group proposed MDR-GAN, a generative adversarial network combining multi-scale convolutional and null convolutional residual networks. This innovation significantly reduced computational load while improving the network's learning ability [21]. To address real-world complex noise more effectively, [PERSON] et al. introduced a novel dual-branch residual attention network (DRANet), which captures complementary features to enhance the model's learning ability [22]. [PERSON] et al. proposed a network integrating dual-CNN with an attention mechanism (DCANet), comprising a noise estimation network, a spatial and channel attention module (SCAM), and a dual-CNN with a two-branch structure, which effectively suppresses both synthetic and real-world noise [23]. [PERSON] et al. proposed a cGAN-based 3D scatter suppression framework, DL-TNode-3D, to achieve efficient denoising by combining partial volume inputs with standard data generated by TNode [24]. [PERSON] et al. proposed a physics-based PSN framework for OCT denoising, integrating dual-module noise synthesis with physical priors to generate paired data, achieving denoising while preserving structural details [25]. Compared to unsupervised denoising algorithms, supervised deep learning algorithms have been shown to achieve superior denoising effects with fewer data points. However, retinal images utilized for clinical diagnosis require not only the elimination and smoothing of granular speckle noise but also the retention of rich structural features and clear textures in the denoised images. Existing methods typically face challenges in balancing noise suppression and detail preservation. Existing methods have demonstrated that U-Net-based approaches combined with GANs can effectively suppress speckle noise but typically introduce excessive smoothing that compromises key organizational structures. The effect of this over-smoothing stems from the limited ability of the architecture to retain structural details while removing noise. To address these limitations, we propose an adversarial generative network (MFFA-GAN) with multilevel feature extraction and an attention mechanism for OCT image noise reduction. The model innovatively integrates a multilevel feature interaction mechanism and a dynamic attention guidance strategy. The multilevel feature fusion module is proposed to be embedded at each level of the U-Net encoding path. The module establishes cross-layer feature interactions through residual connectivity and dense feature concatenation, enabling the network to preserve low-level texture details and high-level semantic information. It also combines the ELA attention mechanism with the multilevel feature fusion module for the first time, which effectively suppresses non-significant feature responses and enhances the efficiency of key information transfer through a dynamic feature channel weighting strategy. The specific approach is to prioritize large-area structural regions in the global context-aware attention map generated from the encoder output and locally enhance feature selection by optimizing spatial details through adaptive thresholding, reducing redundant feature learning while preserving key morphological features.