Although supervised deep learning (DL) offers a potent solution for removing noise from seismic records, challenges are encountered owing to the scarcity of noise-free labels. The innovative Noise2Noise method eliminates the need for clean training targets and extends the applicability of deep learning to seismic data denoising. In this study, we introduce the Noise2Noise Enhancement (N2NE) framework, which improves upon the conventional noise reduction methods used in seismic processing. The applicability of this framework was quantitatively examined using actual field noise under two scenarios: with and without repeated shots. In scenarios with repeated shots, the N2NE framework enhances the conventional stacking method. In addition, the substack strategy, which employs smaller substacks for preliminary noise suppression before DL training, boosts noise suppression. In scenarios without repeated shots, the N2NE framework refines conventional denoising methods (F-X deconvolution) by utilizing information from the common-shot and receiver domains. The N2NE framework lays a foundation for future research on N2N-based seismic denoising methods and contributes to improving the quality of seismic records and the efficiency of data acquisition.