High fidelity medical image super-resolution based on Medical Multi-Feature Compensation Attention GAN

MRI and CT medical images play a crucial role in clinical medicine, and high-resolution images enhance diagnostic quality. Improving image resolution through hardware upgrades is often expensive and may increase radiation exposure for patients. Common super-resolution methods used for natural images do not account for the unique relationships between colors and pixels in medical images. In this paper, we present a novel approach to medical image super-resolution using the Medical Multi-Feature Compensation Attention GAN (MMSRGAN). The proposed method integrates a feature compensation attention module, enhancing the reconstruction of high-resolution images by addressing the unique characteristics and correlations in medical data. Extensive experiments on IXI-T1 datasets demonstrate that MMSRGAN significantly outperforms existing deep learning-based methods, achieving state-of-the-art results in terms of PSNR and SSIM. This advancement highlights the potential for improved diagnostic accuracy and treatment planning in clinical settings through enhanced medical imaging.