UWHFormer: A Spectrally Coherent Transformer for Underwater Hyperspectral Image Restoration

Hyperspectral imaging captures dense spatial–spectral information, enabling fine-grained material identification and scene understanding. Underwater acquisition, however, is degraded by wavelength-dependent attenuation, non-uniform scattering, insufficient illumination, and sensor noise, which jointly corrupt spatial structures and spectral signatures. We formulate underwater hyperspectral image (HSI) restoration as a physics-guided spatial–spectral reconstruction problem and propose UWHFormer, a spectrally coherent Transformer for restoring real-world UnderWater HSIs. UWHFormer first employs a water-conditioned state modeling module to estimate physically interpretable, wavelength-dependent degradation states. A physics-guided restoration transformer then integrates these states with image features to condition residual reconstruction, enabling adaptive compensation for attenuation and scattering while retaining reliable information from the observations. We further introduce a spectral–spatial refinement module that jointly aggregates spectral and spatial neighborhoods to suppress local artifacts without distorting spectral profiles. For systematic evaluation, we construct a paired real-world underwater HSI dataset spanning diverse objects, imaging distances, and degradation levels. Extensive experiments on this dataset show that UWHFormer achieves state-of-the-art performance, with a PSNR of 39.70, SSIM of 0.860, SAM of 1.339 and ERGAS of 4.34, which indicates improvements in both spatial reconstruction and spectral fidelity across a range of underwater conditions.