Brain-MRI inpainting replaces a masked region of a scan with synthesized, anatomically plausible healthy tissue, so that analysis tools built for healthy brains can be applied to images they would otherwise reject. On the BraTS local-synthesis benchmark, which ranks submissions on the structural similarity index (SSIM), the peak signal-to-noise ratio, and the mean squared error (MSE) jointly, the strongest recent models are accurate, but several report blurry synthesized regions and attribute this to the mean-seeking behavior of the
ℓ1 and MSE terms in their training losses. We address this in post-processing, forming a deep ensemble of the two co-first-place 2025 models and training a lightweight residual refiner on the ensemble's own outputs under an
ℓ1 loss augmented with a structural-similarity term whose weight
λ we vary. At a moderate
λ the refiner improves SSIM over the ensemble, from
0.8767 to
0.8780 on a held-out reproduction of the official scorer and from
0.8555 to
0.8572 on the official validation leaderboard, with essentially no change in MSE. The gain is small but consistent, improving
62.6% of the held-out cases with a signed-rank
p=2.2×10−7, whereas over-weighting the structural term reverses it. Two ablations bound the effect. Adding any third model to the two-model ensemble degrades it, and classical unsharp masking fails to improve SSIM at any strength (best
0.8765 against
0.8767), so the gain reflects learned rather than indiscriminate sharpening. The result is a cheap, reproducible post-processing stage that improves an already strong ensemble without any large-scale retraining.