SPIR: Symmetric-Probe Image Restoration with Flow-Matching Priors

Flow-matching priors provide clean-image estimates from degraded inputs, but a limited evaluation budget leaves a choice between a single central prediction and aggregation over a noisy neighborhood. We introduce Symmetric-Probe Image Restoration (SPIR), which averages predictions from opposite Gaussian probes and applies a data-consistency correction. The symmetric construction excludes odd perturbation responses while retaining nonlinear neighborhood information. Reusing the probes keeps the sampled prior estimator fixed within each time step. Our analysis identifies conditions under which symmetric probes improve estimation and characterizes the stability of data correction. Experiments on four datasets and five restoration tasks demonstrate competitive fidelity and perceptual quality, with task-dependent trade-offs.