Transform-based　image　codec　follows　the　basic　principle:　the　reconstructed　quality　is　decided　by　the　quantization　level.　Compressive　sensing　(CS)　breaks　the　limit　and　states　that　sparse　signals　can　be　perfectly　recovered　from　incomplete　or　even　corrupted　information　by　solving　convex　optimization.　Under　the　same　acquisition　of　images,　if　images　are　represented　sparsely　enough,　they　can　be　reconstructed　more　accurately　by　CS　recovery　than　inverse　transform.　So,　in　this　paper,　we　utilize　a　modified　TV　operator　to　enhance　image　sparse　representation　and　reconstruction　accuracy,　and　we　acquire　image　information　from　transform　coefficients　corrupted　by　quantization　noise.　We　can　reconstruct　the　images　by　CS　recovery　instead　of　inverse　transform.　A　CS-based　JPEG　decoding　scheme　is　obtained　and　experimental　results　demonstrate　that　the　proposed　methods　significantly　improve　the　PSNR　and　visual　quality　of　reconstructed　images　compared　with　original　JPEG　decoder.　(C)　2011　Elsevier　B.V.　All　rights　reserved.