In this paper we study the regularization with both l1 and total-variation norm for bioluminescence tomography based on radiative transfer equation, compare l1 data fidelity with l2 data fidelity for different type of noise, and propose novel interior-point methods for solving related optimization problems. Simulations are performed to show that our approach is not only capable of preserving shapes, details and intensities of bioluminescent sources in the presence of sparse or non-sparse sources with angular-resolved or angular-averaged data, but also robust to noise, and thus is potential for efficient high-resolution imaging with only boundary data.