In clinical low-field MRI, prolonged data acquisition is impractical, limiting the achievable SNR during imaging. In the absence of ground truth, unsupervised denoising is desirable, but many of them underperform on correlated noise structure of reconstructed MR images. In this work, we present an effective two step training framework for removing correlated MR noise without ground truth. We demonstrate that the proposed approach outperforms the existing denoising methods when applied to the low-field (64mT) diffusion-weighted images and demonstrate that significant noise reduction is possible. 82.5% of processed images were expertly rated clearly/far better overall.