FOTA: Forward-Only Test-Time Adaptation for Multispectral and Hyperspectral Image Fusion

Multispectral and hyperspectral image fusion (MHIF) is an important approach for obtaining high-resolution hyperspectral images. However, in real-world applications, out-of-distribution (OOD) shifts caused by unknown, target-specific physical degradations lead to severe performance degradation in existing pre-trained MHIF models. Most existing test-time adaptation (TTA) methods rely on proxy losses or explicit degradation loops that require computationally intensive iterative backpropagation, which incurs high computational overhead and makes models prone to overfitting in few-shot scenarios. To address this bottleneck, we propose a novel backpropagation-free, architecture-agnostic framework: Forward-Only Test-Time Adaptation (FOTA). Specifically, we first project high-dimensional features into a compact low-rank subspace to reduce the parameter scale. To overcome the slow convergence of zeroth-order optimization, we utilize the analytical closed-form solution of Bures-Wasserstein optimal transport under the multivariate Gaussian assumption for initialization, achieving rapid distribution alignment. Furthermore, we construct a structured prior from the physical observation differences of high-resolution multispectral images to directly guide the search direction. Finally, the algorithm iteratively refines the low-rank prompt via forward-only zeroth-order optimization, ensuring physical imaging consistency without backpropagation. Experiments on five benchmarks demonstrate that FOTA achieves leading adaptation performance. Our code will be released.