Unsupervised Representation Learning for 3-Dimensional Magnetic Resonance Imaging Super-Resolution with Degradation Adaptation
Unsupervised Representation Learning for 3-Dimensional Magnetic Resonance Imaging Super-Resolution with Degradation Adaptation
High-resolution (HR) magnetic resonance imaging is essential in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and costly. Consequently, deep learning-based super-resolution reconstruction (SRR) has emerged as a promising solution for generating super-resolution (SR) images from low-resolution (LR) images. Unfortunately, training such neural …