Diagnose Before Refining: Ground-Truth-Free Self-Diagnostic Refinement for Implicit Neural Representation

Existing INR reconstruction methods typically terminate once optimization converges, even though non-negligible errors may remain at unobserved entries where ground truth is unavailable. This motivates us to investigate whether the remaining errors can be identified and further reduced without access to their ground truth. By the analysis of the post-reconstruction errors, we find that they are often concentrated and exhibit different optimization behaviors, indicating that uniform refinement is insufficient. Motivated by these observations, we propose SDR, a ground-truth-free self-diagnostic refinement framework that exploits out-of-fold (O-O-F) reconstruction to diagnose and adaptively refine the remaining errors. SDR uses cross-validation uncertainty to guide gated global optimization, and subsequently transfers structurally matched signed O-O-F residuals to correct local errors that are no longer effectively reduced by training. We further provide theoretical characterization for residual correction, establishing a sufficient condition for guaranteed error reduction and deriving an estimation-error bound that explicitly characterizes the roles of structural similarity, fold-to-full discrepancy, and residual uncertainty. Extensive experiments across diverse signal modalities, reconstruction methods, and multiple seeds demonstrate consistent and robust superior performance over the basic method, with mechanism, ablation, and sensitivity analysis further validating the consistent effectiveness of the proposed framework.