Sharpening usually doesn’t truly “restore” lost detail. It mostly increases the appearance of sharpness by boosting local contrast around edges: darkening the dark side of an edge and brightening the light side. Many tools do this with edge-detection or convolution-based methods (for example, kernels that emphasize a pixel relative to its neighbors).

Why it only sometimes works: sharpening works best when the image already contains usable edge information. If blur, motion, missed focus, noise, or very low contrast have already smeared or hidden detail, there may not be enough real information left to recover. Some tools also detect brightness edges better than color-only edges.

Why it can’t be perfect: software cannot create unlimited new information from data that was never captured. It can estimate, enhance, or make edges look crisper, but if detail is gone, it’s gone. Pushing sharpening too far also creates visible artifacts such as halos, exaggerated contrast, and an unnatural look.

So the limit is simple: sharpening can enhance existing detail and edge contrast, but it cannot fully reconstruct a perfectly sharp original when the source data is insufficient.