LARC: A General Late-stage Adaptive Reconstruction Corrector for Diffusion Inverse Problems

Diffusion models provide powerful generative priors for solving imaging inverse problems, enabling high-quality reconstructions from incomplete or corrupted measurements. However, further improving their reconstruction quality can be computationally expensive, particularly when refinement involves repeated inner sampling or optimization across noise levels. Motivated by the coarse-to-fine behavior of reverse diffusion sampling, we introduce the **L**ate-stage **A**daptive **R**econstruction **C**orrector (**LARC**), a general corrector that refines image details based on the structure established during early sampling. LARC can be integrated into a broad range of diffusion inverse solvers, preserving the host solver's early sampling trajectory and refining its later stages. At each selected noise level, LARC performs a two-stage correction. The first is measurement-guided initialization, which solves a regularized minimization problem to obtain an initial estimate guided by the observation. The second is preconditioned score refinement, which further refines the initial estimate through score updates with direction-dependent step sizes. Together, these stages combine measurement guidance with the diffusion prior to refine image details through a small number of local updates. Experiments on FFHQ and ImageNet across inpainting, super-resolution, and deblurring demonstrate that LARC outperforms competing baselines in reconstruction accuracy and consistently improves the tested host solvers with limited additional computation.