Disentangled Panorama Super-Resolution with Latitude-Wise Modeling

Panorama super-resolution suffers from entangled content and degradation features, as well as latitude-dependent distortion caused by equirectangular projection. Existing end-to-end methods jointly learn mixed features, limiting their generalization and amplifying artifacts in high-latitude regions. We propose a decoupled dual-branch framework that explicitly separates sample-specific content features from universal degradation patterns. Two dedicated encoders are trained with a cross-sample feature fusion strategy, forcing the degradation branch to capture only content-agnostic information while the content branch focuses on semantic and geometric structures. To handle non-uniform stretching, we design a latitude-wise block with adaptive convolution whose dilation rate is dynamically modulated by the spherical polar angle. Edge-guided spatial attention and a composite loss with feature consistency further enhance detail recovery and training stability. Our method achieves state-of-the-art performance on standard benchmarks.