## 3 Results
After a simple analysis, we concluded that the key characteristic of our proposed method lies in its ability to smooth the initial signal, which can eliminate the influence of noise to a certain extent and thus has high robustness. We also conducted simulations for multiple sources. The distribution of multiple sources is shown in Figure 12(a). When there are multiple sound sources within the imaging area, the complete photoacoustic signal obtained through DASE is no longer a distinct N-shaped signal, as shown in Figure 12(b). Throughout the whole imaging period, the signal shape appears as an irregular and fluctuating one. Considering that the signals occurring at times far from \(t=0\) can be regarded as interference signals, we eliminate this part of the signal and only process the portion deemed as useful signals, as shown in Figure 12(c). Figure 14 shows the comparison results of the reconstructions of multiple sources using different methods. Figure 13(a) is the reconstruction result obtained by using the DAS method, which exhibits strong artifacts. Figure 13(b) shows the reconstruction result obtained by the DMAS method. It can be seen that the artifacts in the image have been suppressed to a certain extent, but there are some relatively obvious layers in the reconstruction result, which makes the reconstruction quality not very high. Figure 13(c) presents the reconstruction using the multi-DASE. Compared with the previous two methods, the artifacts have been well suppressed. However, at the same time, the internal structure of the source has also been suppressed, resulting in the loss of internal structural information. Figure 13(d) is the reconstruction result using our proposed method. It can be observed that our method not only preserves the artifact suppression capability of the multi-DASE method but also addresses the issue of missing internal structural information in the reconstructed source, thereby substantially enhancing the quality of the reconstructed image. We also calculated the SSIM for the reconstruction results of the irregular source to quantify the differences between images reconstructed using different methods, as presented in Table 3. The SSIM value for the DAS method is only 0.0106. In comparison, the DMAS method demonstrates a stronger ability to suppress artifacts, increasing the SSIM to 0.3424. Nevertheless, there remains significant room for improvement. The multi-DASE method exhibits superior performance in artifact suppression, with the SSIM significantly  
Figure 13: Multiple sound source models and signals for multiple sources. (**a**) The model of multiple sound sources has a row of sensors at the very top of the imaging area; (**b**) unprocessed N-shaped signal and envelope signal; (**c**) signal and envelope signal after removing the useless signals. improving to 0.7881. Our proposed method not only preserves the advantages of the multi-DASE method but also addresses the issue of partial information loss observed in the multi-DASE method. Consequently, the SSIM for our proposed method reaches 0.9783, indicating that the reconstruction results achieved using our method represent the optimal outcomes currently available. We also applied the method proposed in this paper to image point-like sources, and the results are shown in Figure 15. In the same imaging area, four point sources are vertically distributed at x = 10.8 mm in the center of the imaging area. The vertical distance between the first point source from the top is 6.8 mm, and the distance between each subsequent point source is 3 mm. The results show that our method can not only deal with the distortion problem that may occur when imaging large-volume sources, but also, like many existing methods, can still exhibit excellent imaging performance when imaging small-volume sources. Figure 14: Reconstruction results of multiple sources using different methods.