Super-Resolution　restoration　is　a　technique　for　estimating　an　unaliased　high-resolution　image　(or　a　sequence)　from　an　aliased　video　sequence　and　combating　additive　noise　and　blurring　due　to　the　finite　detector　size　and　optics.　An　improved　Bayesian　MAP　estimator　with　multi-scale　edge-preserving　regularization　for　super-resolution　restoration　is　proposed.　The　confidence　of　the　motion　estimation　result　is　validated　to　eliminate　motion　artifact.　The　wavelet　representation　of　an　image　is　utilized　to　define　the　spatial　activity　measure　of　the　image,　and　further　to　construct　a　novel　multi-scale　Huber-Markov　model.　The　experimental　results　show　that　the　multi-scale　Huber-Markov　model　can　be　incorporated　into　Bayesian　MAP　estimator　to　preserve　the　edges　of　the　super-resolution　image　effectively.　This　proposed　algorithm　is　widely　used　for　remote　sensing,　medical　imaging,　high-definition　television　(HDTV)　standard　and　creation　of　synthetic　＇video　zoom＇.