G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening

Pansharpening aims to fuse a high-resolution panchromatic(PAN) image and a low-resolution multispectral (LRMS) image to pro-duce a high-resolution multispectral (HRMS) image. Recent deep modelshave achieved strong performance, yet they typically rely on large-scalepretraining and often generalize poorly to unseen real-world image pairs.Prior zero-shot approaches improve real-scene generalization but requireper-image optimization, hindering weight reuse, and the above methodsare usually limited to a fixed scale. To address this issue, we proposeG-ZAP, a generalizable zero-shot framework for arbitrary-scale pansharp-ening, designed to handle cross-resolution, cross-scene, and cross-sensorgeneralization. G-ZAP adopts a feature-based implicit neural representa-tion (INR) fusion network as the backbone and introduces a multi-scale,semi-supervised training scheme to enable robust generalization. Extensiveexperiments on multiple real-world datasets show that G-ZAP achievesstate-of-the-art results under PAN-scale fusion in both visual qualityand quantitative metrics. Notably, G-ZAP supports weight reuse acrossimage pairs while maintaining competitiveness with per-pair retraining,demonstrating strong potential for efficient real-world deployment.