Figure 3 shows the true image and target images reconstructed by VMP-3L, SSCIM, SDA, SACI, and SC-VMP. It can be concluded that our proposed SC-VMP algorithm achieves superior imaging performance over the other four algorithms tested above. As shown in Fig. 6(f), the target is reconstructed perfectly without any spurious scatterers. Comparably, the other four algorithms suffer from imperfect reconstruction performance, and the reconstructed images are defocused with some spurious scatterers and the strength of scattering centers is not reconstructed exactly, although the target profiles are clear, which makes the images recognizable. Comparing the list algorithms, the difference is mainly focused on the method to reconstruct the scattering coefficients (see Sec. 3.5 for details). For SSCIM, SDA, and SACI, they suffer from the effect of phase error and the signal energy spills over the imaging plane because of the dictionary mismatch caused by phase error, as shown in Figs. 3(c)–3(e), while our proposed SC-VMP utilizes the hierarchical modeling procedure and is considered as a full Bayesian method. More importantly, the method is less sensitive to the phase error, as shown in Fig. 3(b). In addition, the phase error is estimated and compensated perfectly by the SC-VMP algorithm, as shown in Fig. 4. Then, we could conclude that exploiting the phase error improves the performance of SC-VMP. Consequently, benefiting from full utilization of the sparsity prior and phase error calibration, the image quality shown in Fig. 3(f) is improved significantly, compared with the images reconstructed by other algorithms.