This　paper　interprets　image　interpolation　as　a　decoding　problem　on　tanner　graph　and　proposes　a　practical　belief　propagation　algorithm　based　on　a　gaussian　autoregressive　image　model.　This　algorithm　regards　belief　propagation　as　a　way　to　generate　and　fuse　predictions　from　various　check　nodes.　A　low　complexity　implementation　of　this　algorithm　measures　and　distributes　the　departure　of　current　interpolation　result　from　the　image　model.　Convergence　speed　of　the　proposed　algorithm　is　discussed.　Experimental　results　show　that　good　interpolation　results　can　be　obtained　by　a　very　small　number　of　iterations.