WANSR: Distilling Video Diffusion for Trajectory-Aware Real-World Image Super-Resolution

Real-world image super-resolution (Real-ISR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs perturbed by unknown and mixed degradations. Although diffusion-based methods enable generating perceptually satisfying results, their intermediate denoising states are typically treated as numerical variables for sampling, making it hard to interpret and explicitly constrain the restoration process. Therefore, we ask the question: Can the intermediate restoration process be explicitly modeled? In this paper, we propose WANSR, a trajectory-aware framework that reformulates Real-ISR as a conditional video generation problem. Instead of directly predicting a single HR image, WANSR models a sequence of restoration states that explicitly describes the progressive evolution toward the final high-quality distribution. To stabilize this evolution, we introduce anchor-guided trajectory alignment by aligning transition directions and anchor-level magnitudes. We further propose quality-verified trajectory refinement, which evaluates restoration progress of each latent state and enforces monotonic quality improvement along the generated trajectory. Extensive experiments on multiple Real-ISR benchmarks demonstrate that our WANSR achieves superior reconstruction performance, while explicitly revealing the progressive process of degradation removal and details reconstruction.