G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening
This addresses the need for efficient and generalizable pansharpening methods for remote sensing applications, though it is incremental as it builds on prior zero-shot approaches.
The paper tackles the problem of pansharpening, which fuses high-resolution panchromatic and low-resolution multispectral images, by proposing G-ZAP, a generalizable zero-shot framework that achieves state-of-the-art results in visual quality and quantitative metrics on multiple real-world datasets.
Pansharpening aims to fuse a high-resolution panchromatic (PAN) image and a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral (HRMS) image. Recent deep models have achieved strong performance, yet they typically rely on large-scale pretraining and often generalize poorly to unseen real-world image pairs.Prior zero-shot approaches improve real-scene generalization but require per-image optimization, hindering weight reuse, and the above methods are usually limited to a fixed scale.To address this issue, we propose G-ZAP, a generalizable zero-shot framework for arbitrary-scale pansharpening, designed to handle cross-resolution, cross-scene, and cross-sensor generalization.G-ZAP adopts a feature-based implicit neural representation (INR) fusion network as the backbone and introduces a multi-scale, semi-supervised training scheme to enable robust generalization.Extensive experiments on multiple real-world datasets show that G-ZAP achieves state-of-the-art results under PAN-scale fusion in both visual quality and quantitative metrics.Notably, G-ZAP supports weight reuse across image pairs while maintaining competitiveness with per-pair retraining, demonstrating strong potential for efficient real-world deployment.