Learning Heterogeneous Degradation Representation for Real-World Super-Resolution

Real-World Super-Resolution (RWSR) aims to reconstruct high-resolution images from low-resolution inputs captured under complex, real-life conditions, where diverse distortions result in significant degradation heterogeneity. Many methods rely on degradation representations, yet they struggle with the lack of spatially variant degradation modeling and degradation-content entanglement. We propose Spatially Amortized Variational Learning (SAVL), an implicit framework that models per-pixel degradations as spatially varying Gaussians inferred from local neighborhoods. SAVL couples a conditional likelihood lane (SAVL-LM) with a mutual information suppression lane (SAVL-MIS) to filter out degradation-irrelevant signals, yielding a well-constrained solution space. Both our qualitative visualizations and quantitative analyses confirm that the learned representations effectively capture the spatial distribution of complex degradations while being highly discriminative of diverse underlying degradation factors. Building on these representations, we design a degradation-aware SR network with channel-wise guidance and spatial attention modulation for adaptive reconstruction under heterogeneous degradations. Extensive experiments on real-world datasets demonstrate consistent gains over prior methods.