Kernel Space Diffusion Model for Efficient Remote Sensing Pansharpening

Pansharpening is a fundamental task in remote sensing that integrates high-resolution panchromatic imagery (PAN) with low-resolution multispectral imagery (LRMS) to produce an enhanced image with both high spatial and spectral resolution. Despite significant progress in deep learning-based approaches, existing methods often fail to capture the global priors inherent in remote sensing data distributions. Diffusion-based models have recently emerged as promising solutions due to their powerful distribution mapping capabilities; however, they suffer from significant inference latency, which limits their practical applicability. In this work, we propose the Kernel Space Diffusion Model (KSDiff), a novel approach that leverages diffusion processes in a latent space to generate convolutional kernels enriched with global contextual information, thereby improving pansharpening quality while enabling faster inference. Specifically, KSDiff constructs these kernels through the integration of a low-rank core tensor generator and a unified factor generator, orchestrated by a structure-aware multi-head attention mechanism. We further introduce a two-stage training strategy tailored for pansharpening, enabling KSDiff to serve as a framework for enhancing existing pansharpening architectures. Experiments on three widely used datasets, including WorldView-3, GaoFen-2, and QuickBird, demonstrate the superior performance of KSDiff both qualitatively and quantitatively. Code will be released upon possible acceptance.