Magnetic resonance imaging (MRI) is powerful in medical diagnostics, yet high-field MRI, despite offering superior image quality, incurs significant costs for procurement, installation, maintenance, and operation, restricting its availability and accessibility, especially in low- and middle-income countries. Addressing this, our study proposes an unsupervised learning algorithm based on cycle-consistent generative adversarial networks. This framework transforms 0.3T low-field MRI into higher-quality 3T-like images, bypassing the need for paired low/high-field training data. The proposed architecture integrates two novel modules to enhance reconstruction quality: (1) an attention block that dynamically balances high-field-like features with the original low-field input, and (2) an edge block that refines boundary details, providing more accurate structural reconstruction. The proposed generative model is trained on large-scale, unpaired, public datasets, and further validated on paired low/high-field acquisitions of three major clinical MRI sequences: T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) imaging. It demonstrates notable improvements in tissue contrast and signal-to-noise ratio while preserving anatomical fidelity. This approach utilizes rich information from publicly available MRI resources, providing a data-efficient unsupervised alternative that complements supervised methods to enhance the utility of low-field MRI.