A permanent magnet based low-field MRI system provides portability and affordability. However, the quality of the image is low due to a low signal-to-noise ratio (SNR). We propose a new deep learning structure which effectively integrates denoising-networks end-to-end to super-resolution-networks, to transfer the rich information available from one-oﬀ experimental imaging from a mid-field MRI scanner (1.5T) to the lower-quality data from a portable system. The procedure uses matched pairs to learn mappings from low-quality to the corresponding high-quality images. Using the proposed method, the quality and resolution of an image from a low-field MRI system is significantly improved.