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Magnetic Resonance Imaging (MRI) is an important technique in the clinic. Fast MRI based on k-space undersampling and high-quality image reconstruction has been widely utilized, and many deep learning-based methods have been developed in recent years. Although promising results have been achieved, most existing methods require fully-sampled reference data. Unfortunately, fully-sampled MRI data are difficult to obtain in real-world applications. To address this issue, we propose a data refinement framework for self-supervised MR image reconstruction. Specifically, we first analyze the reason of the performance gap between self-supervised and supervised methods and identify that the bias in the training datasets is one major factor. Then, we design an effective self-supervised data refinement learning framework to reduce this data bias. With the data refinement, an enhanced self-supervised MR image …