Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations

Cross-modal super-resolution (SR) on real-world misaligneddata is challenging, as only unlabeled low-resolution (LR) source andhigh-resolution (HR) guide images with complex spatial misalignmentare available. Previous methods either rely on simulated training dataor adopt suboptimal alignment strategies that overlook cross-modal de-pendencies, limiting their practical performance. To address these is-sues, we propose RobSelf, a self-supervised model that jointly optimizesa misalignment-aware feature translator and a content-aware referencefilter online. The translator resolves unsupervised cross-modal and cross-resolution alignment via weakly-supervised, misalignment-aware trans-lation, yielding an aligned guide feature. Guided by this feature, thefilter performs reference-based discriminative self-enhancement on thesource, enabling SR prediction with high resolution and high fidelity.Experiments on synthesized data and collected real-world data demon-strate that RobSelf achieves state-of-the-art performance, outperformingexisting self-supervised and supervised methods. Moreover, it achievessuperior efficiency, being up to 15.3× faster than prior self-supervisedmethods. https://github.com/palmdong/RobSelf