Bioluminescence　imaging　(BLI)　is　an　optical　molecular　imaging　modality　for　monitoring　physiological　and　pathological　activities　at　the　molecular　level.　The　information　of　bioluminescent　probe　distribution　in　small　animals　can　be　three-dimensionally　and　quantitatively　obtained　by　bioluminescence　tomography　(BLT).　Due　to　ill-posed　nature,　BLT　may　bear　multiple　solutions　and　aberrant　reconstruction　in　the　presence　of　measurement　noise　and　optical　parameter　mismatches.　Among　different　regularization　methods,　L2-type　regularization　strategy　is　the　most　popular　and　commonly-applied　method,　which　minimizes　the　output-least-square　formulation　incorporated　with　the　l2-norm　regularization　term　to　stabilize　the　problem.　However,　it　often　imposes　over-smoothing　on　the　reconstruction　results.　In　contrast,　for　many　practical　applications,　such　as　early　detection　of　tumors,　the　volumes　of　the　bioluminescent　sources　are　very　small　compared　with　the　whole　body.　In　this　paper,　L1　regularization　is　used　to　fully　take　advantage　of　the　sparsity　prior　knowledge　and　improve　both　efficiency　and　stability.　And　then　a　reconstruction　method　based　on　the　augmented　Lagrangian　approach　is　proposed,　which　considers　the　BLT　problem　as　the　constrained　optimization　problem　and　employs　the　Bregman　iterative　method　to　deal　with　it.　By　using　＂divide　and　conquer＂　approach,　the　optimization　problem　can　be　exactly　and　fast　solved　by　iteratively　solving　a　sequence　of　unconstrained　subproblems.　To　evaluate　the　performance　of　the　proposed　method　in　turbid　mouse　geometry,　stimulate　experiments　with　a　heterogeneous　3D　mouse　atlas　are　conducted.　In　addition,　physical　experiments　further　demonstrate　the　potential　of　the　proposed　algorithm　in　practical　applications.