Low-Rank Gradient Guidance With Mutual-Guided Mamba for Hyperspectral Pansharpening

Hyperspectral (HS) pansharpening is a key preprocessing step in various remote sensing applications, which produces a High-Resolution (HR) HS image through the fusion of a Low-Resolution (LR) HS image with a High-Resolution (HR) Panchromatic (PAN) image. Recent deep learning-based methods, particularly those employing convolutional neural networks and Transformers, have achieved impressive progress owing to their strong representational capacity. However, the fusion performance of these methods may degrade in the absence of large-scale datasets. Moreover, the attention mechanism in Transformers results in quadratic computational complexity. To alleviate these limitations, we propose MPSRNet-Diff, a two-stage fusion framework that avoids directly learning the relationship between observed images and ideal HR-HS images. In the first stage, a prior HS image is generated using a pretrained diffusion model, which benefits from strong generalisation capabilities learned from large-scale datasets. To improve the efficiency of generating this prior HS image, the low-rank property of HS data is exploited, and a principal component analysis-based band selection strategy is proposed to reduce spectral dimensionality. This prior HS image serves as a PAN-like proxy, guiding the HR-HS reconstruction by regularising the solution space and improving the generalisation ability to unseen data. In the second stage, a Mamba-based Progressive Super-Resolution Network (MPSRNet) is proposed to enhance the spatial details of the HS image in a step-by-step manner to learn global dependencies without quadratic complexity. At each refinement step, the MPSRNet receives the prior HS image, PAN image, and HS image to be super-resolved as inputs. To fully exploit both intra-input and inter-input global dependencies, a mutual-guided Mamba block is introduced, which leverages state space models for efficient global information modelling. Extensive experiments on four datasets demonstrate that MPSRNet-Diff is competitive with state-of-the-art techniques in both quantitative metrics and visual quality, validating its effectiveness and robustness.