GSAIOIR: Exploring Gaussian Fields for All-in-One Image Restoration

Gaussian primitives have advanced 3D scene representation and have shown promise for image-plane modelling. We investigate whether explicit Gaussian primitives can serve as a shared output interface for image restoration and downstream prediction. Existing formulations address task-specific objectives, motivating all-in-one restoration as a setting for testing this interface across degradation operators. Two challenges arise. At fixed primitive density, larger images require more Gaussians, motivating crop-wise prediction with limited global context. Additive rendering depends on accumulated weight, whereas normalised blending with nonnegative weights confines a single RGB field to a convex average of its colours. We introduce GSAioIR, a task-conditioned Gaussian restoration framework. A bounded full-image view and crop-box geometry provide aligned context for local primitive prediction. Three parallel fields produce coarse RGB and signed residuals at the middle and fine scales. Independent normalisation removes uniform weight-scale dependence. Residual summation then permits corrections outside the coarse field's colour convex hull, while query-key matching makes fine-scale aggregation content-dependent. Experiments support restoration across five degradation types. Downstream transfer extends the backbone to segmentation, detection, depth, and surface-normal prediction. These results support the applicability of 2D Gaussian representations across restoration and downstream vision tasks.