Accurately capturing the complex structures of human body components such as the bones, lungs, and abdominal organs is essential for diagnostic applications. Computed tomography (CT) enables detailed imaging; however, excessive radiation exposure poses health risks, whereas lower radiation levels introduce artifacts, degrading image quality. To address these challenges, we propose multipath feature fusion encoder-decoder super-resolution (MF2EDSR), a multipath encoder-decoder network designed for medical image super-resolution. The model integrates a coarse-to-fine learning strategy, allowing progressive feature refinement through a hierarchical manner. Our approach utilizes a two-path feature fusion mechanism, where a primary path focuses on large-scale structural details, and a refinement path enhances finer textures. To further improve feature representation, the feature extraction residual network is designed as a decoder. This incorporates a global enhancement module that preserves low-frequency global structures and a local enhancement module that refines high-frequency textures. Skip connections ensure stable gradient flow, mitigating the vanishing gradient issue. Extensive experiments on benchmark datasets, including medical CT, MRI, and natural image datasets, demonstrate the effectiveness of our method. The proposed MF2EDSR architecture outperforms state-of-the-art models. The results confirm that our model excels in reconstructing high-quality images with superior edge definition and minimal artifacts, making it a promising solution for medical image enhancement.