Retrieving spatial distributions of cloud liquid water content (LWC) from the airborne double-antenna microwave radiometer data by tomography method is a limited-angle image reconstruction. In order to enhance the well-posedness of this inverse problem, this paper improves the current regularization methods through using the discrete form of the norm in W1, 2 space as the regular term according to the characteristic of LWC distribution and choosing (30°, 90°) as the best combination of the double antenna elevation angles according to the results of numerical simulations. The L-BFGS-B algorithm is used to solve the nonlinear optimization problem to avoid the model error caused by the linearization. The retrieval model falls into two types according as the LWC distribution in the lateral boundary is known or unknown and they are called Yijie model and Weijie model for short. The retrieval results of the Yijie model show that the relative error is 8.6%－12.3% and the structure of different cloud types can be showed in the retrieval images. Sensitivity studies indicate that the number of scanning angles, the radiometer noise level, and the uncertainty of LWC in the lateral boundary affect the retrieval accuracy most. Besides, the retrieval accuracy also varies in different cloud types because of the magnitude of the LWC, the characteristic of the regular operator, and the lateral boundary factor. In order to make the remote sensing of LWC distributions independent of other instruments, the nesting retrieval method is developed to retrieve the Weijie model. The numerical simulation shows that the nesting method can offer enough information of the lateral boundary for retrieving the LWC distribution in the target area.