GeoHaze: Geometry-Conditioned Spectral Propagation for Structure-Preserving Image Dehazing

Image dehazing in severe natural haze and remote-sensing atmospheric degradation suffers from severe information suppression, making structure-consistent restoration depend on information beyond the degraded neighborhood. Monocular depth provides such nonlocal geometry, yet depth inferred from hazy inputs may contain shifted or missing boundaries; directly fusing it with RGB features can therefore entangle geometric errors with the appearance being reconstructed. We introduce GeoHaze based on a different principle: uncertain geometry should condition how RGB evidence propagates, rather than serve as reconstruction content. GeoHaze realizes this principle through a projected damped-wave spectral operator whose RGB state is driven by a spatially gated depth response, enabling global interaction while retaining RGB as the appearance carrier. Within this operator, two-level RGB–depth wavelet statistics predict sample-wise wave speed and damping, while a residual gate regulates how much of the propagated response re-enters the RGB stream; a frequency-decoupled decoder then uses geometric guidance to separately recover low-frequency layout and refine high-frequency detail, translating wave responses into structurally coherent reconstructions. Across 11 natural and remote-sensing benchmarks, GeoHaze achieves competitive restoration quality.Controlled wave variants and depth perturbations characterize the contribution and sensitivity of geometry conditioning, with an approximately 64% reduction in mean PSNR drop relative to SSDG across the three evaluated depth corruptions on RW²AH. Code is available in the supplementary materials.