Edge Map Reinforced Two Stage Super-Resolution GAN with Attention-Based DenseNet Generator for Knee MR Images

Motivation: Super-resolution is challenging especially in medical imaging where recovering fine structures from low-resolution images is essential for accurate diagnosis. Goal(s): Our goal is to achieve super-resolution for the low-resolution knee MR images. Approach: We propose an edge map reinforced super-resolution GAN with attention-based DenseNet generator to enhance the knee structures in two stages: (1) Generating an enhanced edge map for the low-resolution input image; (2) Generating super-resolution output by using the enhanced edge map generated in the first stage and low-resolution input images. Results: The results show that the proposed method provides better output than the contemporary super-resolution GAN method. Impact: This work presents a robust edge map reinforced super-resolution framework for knee MRI by using an attention-based DenseNet generator in super-resolution GAN. The proposed method generates MR images with clear edges and facilitates accurate medical assessment for knee related disorders.