DuCoFPan: Dual-Condition Flow-based Network for Pan-sharpening

Pan-sharpening aims to reconstruct high-resolution multi-spectral (HR-MS) images from panchromatic (PAN) images and low-resolution multi-spectral (LR-MS) images. Despite demonstrated performance, existing learning-based methods struggle to address the ill-posed problem from spectral and spatial perspectives. Generally, mitigating the ill-posed issue involves obtaining the probability distribution of HR-MS images. In this paper, we propose DuCoFPan, a novel dual-condition flow-based network for pan-sharpening, which learns the distributions of HR-MS images guided by spectral and spatial conditions, respectively. Specifically, we design a Dual-Condition Flow Module (DCFM) that adopts spectral and spatial conditions through reversible affine transformations. For condition injection, we present a Spectral-based Condition Injection Block (SPEB) capturing fine-grained spectral features in the Fourier domain and a Spatial-based Condition Injection Block (SPAB) extracting spatial features. Additionally, we devise a Feature Interaction Block (FIB) to promote information flow. Extensive experiments on QuickBird and GaoFen-2 datasets demonstrate the effectiveness and superiority of our method.