磁共振成像（MRI）作为一种重要的临床影像技术，具有无电离辐射、高软组织对比以及多方位多参数成像等优势，已广泛应用于疾病筛查、临床诊断和疗效评估. 然而，其成像过程受采集条件与硬件系统等因素影响，图像受到不同程度的噪声污染. 本文提出一种自步学习（SPL）驱动的无训练深度生成式磁共振图像去噪方法：自步深度图像先验（SP-DIP）. 具体而言，SP-DIP以经典的深度图像先验（DIP）框架为基础，根据图像块在网络优化过程中的拟合难度动态分配权重，使优化初期更多依赖数据一致性较好、结构较稳定的区域来引导参数更新，而后续迭代中逐步引入复杂纹理、细节边缘以及噪声敏感区域，从而实现更加稳健的渐进式去噪策略. 不同数据集与多种噪声水平的对比实验表明，SP-DIP在去噪精度方面优于传统迭代滤波类方法，同时在强噪声场景下相较于有监督深度学习方法依然表现出竞争力. 

Magnetic resonance imaging (MRI) is an important clinical imaging modality that offers advantages such as the absence of ionizing radiation, high soft-tissue contrast, and multiplanar and multiparametric imaging capabilities. It has been widely used in disease screening, clinical diagnosis, and treatment response assessment. However, MRI acquisition is affected by factors such as scanning conditions and hardware systems, resulting in varying degrees of noise contamination. To address this problem, this paper proposes a self-paced learning (SPL)—driven training-free deep generative method for MR image denoising, termed self-paced deep image prior (SP-DIP). Specifically, built upon the classical deep image prior (DIP) framework, SP-DIP partitions the observed image into patches and dynamically assigns weights to them according to their fitting difficulty during network optimization. Patches with better data consistency and more stable structures are preferentially used to guide parameter updates in the early stages, while patches containing complex textures, fine edges, or noise-sensitive regions are gradually introduced in subsequent iterations, thereby enabling a more robust progressive denoising strategy. Comparative experiments on multiple datasets under various noise levels demonstrate that SP-DIP outperforms traditional iterative filtering methods in terms of denoising accuracy and remains competitive with supervised deep learning methods under high-noise conditions.