When Restoration Becomes the Reference: Reusing Full-Reference IQA in Blind Settings

Full-reference image quality metrics are consistently stronger than blind ones, but they are unusable in practice because the pristine reference is rarely available. This work closes that gap by synthesising a reference instead of requiring one: a restoration model reconstructs a pseudo-reference from the distorted input, which a full-reference metric can then score.

The resulting pipeline lets well-established full-reference measures operate in genuinely blind settings, and we analyse when the restored reference is faithful enough for the borrowed metric to stay reliable.