Unpaired All-in-One Weather Restoration via Structured Stochastic Updates in Schrödinger Bridges

Unpaired all-in-one weather image restoration requires a single generative model to learn restoration mappings for multiple degradations from non-corresponding degraded and clean images. Unpaired Neural Schrödinger Bridge progressively constructs restoration paths by predicting clean endpoints at different bridge positions. However, its original training estimates gradients using only one complete stochastic realization at a randomly sampled bridge position, without integrating conditional gradient information from multiple stochastic realizations in the joint parameter space of the generator and the trainable feature network. To this end, we propose Structured Stochastic Update Schrödinger Bridge (SSUSB). Conditional Gradient Ensembling (CGE) estimates joint gradients of the generator and feature network from multiple complete stochastic realizations and constructs a consensus using their mean. Bridge Stratified Coupling (BSC) further implements joint sampling without replacement while preserving the uniform marginal distribution of the bridge position for each view. Consensus Tangent-space Projection (CTP) uses the multi-view consensus to calibrate a prespecified candidate gradient. In a frozen Adam-derived metric, it replaces the candidate’s axial component with the consensus while preserving its orthogonal component. Extensive experiments demonstrate that SSUSB can effectively improve unpaired all-in-one weather image restoration performance.