HARC-Net: Hierarchical Multiaxis Representation and Adaptive Residual Calibration for End-to-End SAR-to-Optical Image Translation
HARC-Net is presented, an end-to-end Transformer-based regression framework that combines hierarchical multiaxis representation learning with statistics-guided skip-feature calibration to improve robustness and reconstruction quality in remote sensing applications requiring geometrically consistent and noise-resilient optical reconstruction under adverse imaging conditions.