For fluorescence molecular tomography (FMT), image quality could be improved by incorporating a sparsity constraint. The L1 norm regularization method has been proven better than the L2 norm, like Tikhonov regularization. However, the Tikhonov method was found capable of achieving a similar quality at a high iteration cost by adopting a zeroing strategy. By studying the reason, a Tikhonov-regularization-based projecting sparsity pursuit method was proposed that reduces the iterations significantly and achieves good image quality. It was proved in phantom experiments through time-domain FMT that the method could obtain higher accuracy and less oversparsity and is more applicable for heterogeneous-target reconstruction, compared with several regularization methods implemented in this Letter.