Statistical Latent Manifold-Guided Framework for Generative Super-Resolution

Abstract

We propose a statistical latent manifold-guided framework for generative super-resolution to recover fine image details from limited low-resolution inputs. The method combines synthesized multi-view cues and statistical manifold constraints, improving structural fidelity and robustness in medical imaging-oriented super-resolution tasks.