This　article　is　concerned　with　model　averaging　for　de-noise　linear　models,　in　which　some　covariates　are　not　observed,　but　their　ancillary　variables　are　available.　The　least-squares-based　estimation　procedure　is　used　to　estimate　the　unknown　regression　parameter　in　each　candidate　model　after　the　calibrated　error-prone　covariates　are　obtained.　Then　a　Mallows-type　weight　choice　criterion　is　constructed.　When　all　candidate　models　are　misspecified,　the　model　averaging　estimator　is　asymptotically　optimal　in　the　sense　that　achieving　the　lowest　possible　squared　error.　On　the　other　hand,　when　the　true　model　is　included　in　the　set　of　candidate　models,　the　model　averaging　estimator　of　the　regression　parameter　is　root　n　consistent.　The　finite　sample　performance　of　our　model　averaging　estimator　is　evaluated　by　some　simulation　studies.　The　proposed　procedure　is　further　applied　to　real-data　analysis.