A　novel　Bayesian　compressive　sensing　image　reconstruction　algorithm　based　on　the　context　modeling　is　proposed　to　solve　the　problem　that　the　intrascale　dependencies　of　image＇s　wavelet　coefficients　is　not　fully　exploited　by　the　compressive　sensing　reconstruction　algorithm.　It　is　assumed　that　the　wavelet　coefficients　of　image　obey　a　spike-and-slab　probability　model.　Context　vectors　in　the　current　coefficient＇s　neighborhood　are　obtained　through　a　new　context　modeling　method.　Then,　the　significant　probability　of　current　coefficient　is　estimated　according　to　the　dependencies　of　the　context　vector　with　the　current　coefficient　and　the　state　of　parent　coefficient.　Finally,　the　image＇s　wavelet　coefficients　is　recovered　from　the　observation　vector　based　on　the　significant　probabilities　of　the　image＇s　wavelet　coefficients　by　using　a　Bayesian　inference　via　Markov　chain　Monte　Carlo　sampling,　thus,　the　reconstruction　image　is　generated.　Since　the　spike-and-slab　probability　model　with　context　modeling　is　adaptive　to　the　spatial　changes　of　the　images,　experimental　results　and　comparisons　with　Bayesian　tree-structured　wavelet　compressive　sensing　algorithm　which　only　uses　the　interscale　dependencies　show　that　the　proposed　algorithm　improves　the　peak-signal-to-noise-ratio　nearly　by　2　dB　at　the　sampling　rate　of　0.9.