In　recent　years,　the　restoration　of　computerized　tomography　(CT)images　with　low-dose　projection　is　a　key　issue　in　CT　image　processing.　The　sparse　views-based　methods　have　been　proposed　to　achieve　reasonable　image　quality.　This　paper　studies　three　conventional　sparse-view　CT　image　reconstruction　algorithms:　the　total　variational　minimization　projection　onto　convex　set　(TVM-POCS)　algorithm,　the　two-step　iterative　Shrinkage-Thresholding　(TwIST)　algorithm,　and　the　iterative　filtered　back　projection　(FBP)　algorithm.　The　three　algorithms　are　compared　and　analyzed　in　terms　of　the　computational　complexity,　universal　quality　index(UQI),　and　structure　similarity　index(SSIM).　Two　experiments　with　comparison　are　performed　in　the　case　of　sparse-view　and　low-dose　projection,　respectively.　The　computed　results　reveal　that　under　Poisson　noise　environments,　the　TVM-POCS　algorithm　has　superior　performance　over　other　algorithms　in　restoration　quality　and　computing　time.