The　continuous　emission　of　spike　stream　offers　more　significant　advantages　over　traditional　fixed　low　sampling　rate　cameras.　Although　many　reconstruction　methods　from　spike　streams　have　been　proposed,　the　quality　of　recovered　images　remains　suboptimal.　Coarse-to-fine　high-speed　motion　scene　reconstruction　reconstructs　the　spike　sequence　by　dividing　the　dynamic　and　static　regions.　However,　issues　of　limited　texture　richness　and　low　contrasts　are　unsolved　during　the　reconstruction　of　static　spike.　To　address　these　issues,　we　propose　a　Coarse-to-Fine　spatio-temporal　Luminance-Aware　Reconstruction　(CFLAR)　framework.　Specially,　we　propose　an　adaptive　luminance-aware　reconstruction　in　spatio-temporal　domain.　To　be　specific,　in　the　spatial　domain,　we　perform　region　division　and　region　merging　of　spike　sequences　based　on　luminance　information,　while　non-uniform　quantization　of　luminance　information　is　mainly　achieved　through　binary　division　and　adaptive　parameters　division.　In　the　temporal　domain,　we　integrate　alterable　window　length　into　the　texture　from　playback　to　propose　adaptive　time　shift　window　reconstruction,　which　enables　to　obtain　reconstructed　images　with　richer　textures　and　higher　contrasts.　Experimental　results　demonstrate　that　our　CFLAR　method　outperforms　state-of-the-art　approaches　in　terms　of　objective　and　subjective　quality.　©　2024　IEEE.