Complex-Valued Residual Diffusion with GRPO for Pansharpening

Abstract

Pansharpening aims to fuse a high-resolution panchromatic (PAN) image with a low-resolution multispectral (MS) image to generate a highresolution multispectral output that preserves both fine spatial details and faithful spectral responses. However, enhancing spatial textures without introducing spectral distortion remains challenging due to the inherent spectral–spatial trade-off. To address this issue, we propose a two-stage pansharpening framework that tackles the problem from both modeling and optimization perspectives. In the first stage, we formulate pansharpening as spectral-prior conditioned residual diffusion, where a stable spectral base constrains the generation process and allows the diffusion model to focus on PAN-guided high-frequency details, leading to improved training stability and reduced spectral drift. To better capture the coupled spatial and frequency characteristics of PAN–MS fusion, we adopt a complex-valued denoising network to enhance spectral–spatial interaction modeling. In the second stage, to bridge the gap between distortionoriented training objectives and practical quality preferences, we introduce Group Relative Preference Optimization (GRPO) to fine-tune the diffusion model using multi-objective preference signals, explicitly balancing spectral fidelity, texture sharpness, and perceptual quality. Extensive experiments on standard benchmarks demonstrate that the proposed method achieves a more favorable trade-off between fidelity and perceptual quality compared to competitive end-to-end and diffusionbased approaches.