Utilizing　both　intra　and　inter　views　correlation　plays　a　key　role　to　improve　compressive　sensing　reconstruction　of　multi-view　images.　For　this　goal,　this　paper　presents　a　joint　optimization　model　(JOM)　for　compressively-sensed　multi-view　image　reconstruction,　which　jointly　optimizes　an　adaptive　disparity　compensated　residual　total　variation　(ARTV)　and　a　multi-image　nonlocal　low-rank　tensor　(MNLRT).　To　exploit　the　inter-view　correlation　efficiently,　the　ARTV　method　adaptively　forms　suitable　dynamic　image　set　to　help　reconstruct　the　current　one.　Different　from　previous　work,　the　MNLRT　regularization　uses　tensor　rather　than　2D　matrix　to　exploit　nonlocal　low-rank　property,　which　keeps　intrinsic　geometrical　structures　of　image　patches.　An　efficient　algorithm　is　further　proposed　to　solve　the　joint　optimization　problem　via　Split-Bregman　based　technique.　Extensive　experimental　results　demonstrate　our　method　outperforms　state-of-the-arts　algorithms　with　almost　1.5　dB　gain　in　terms　of　PSNR,　while　obtaining　dramatically　improved　visual　quality　for　edge　area,　especially　at　low　sampling　rates.