## 4 Experimental Verification  
Next, MIPO-CSGI is verified experimentally. The experimental setup of MIPO-CSGI is shown in Figure 5. Computer-1 controls DMD-1 (TI DLPC350), modulates the light emitted by an LED, and generates a series of speckle patterns pairs, \(I_{i}^{k}(x,y)\) and \(\widetilde{I}_{i}^{k}(x,y)\). Then, a projector lens projects the speckle patterns onto DMD-2 (TI DLPC350). Computer-2 controls DMD-2 to repeatedly display the object with a cycle time \(T\). A lens (focal length is 50 mm) collects the light reflected from the object, which is detected by a slow bucket detector (Thorlabs PDA100A2), and the paired detection results \(B_{i}\) and \(\widetilde{B}_{i}\) are generated in turn. With the operation repeated \(2\times M\) times, we will obtain \(M\) differential signals \(\{\triangle B_{i}\}_{i=1}^{M}\) by \(B_{i}-\widetilde{B}_{i}(i=1,2,\ldots,M)\). Meanwhile, computer-1 will program matrix \(\mathbf{R}\) based on the \(M\) groups' differential speckle patterns \(\{\triangle I_{i}^{k}(x,y)\}_{i=1}^{M}(k=1,2,\cdots,N)\). Finally, we use the compressive sensing algorithm to obtain multiple images of the target based on matrix \(\mathbf{R}\) and \(\{\triangle B_{i}\}_{i=1}^{M}\) generated by computer-1. Four frames of the jumping \"square\" chosen from Figure 2 are selected as the experimental target to validate MIPO-CSGI's reconstruction ability. In the experiment, DMD-2 displays four frames of 1024 \(\times\) 768-resolution jumping \"square\" with a cycle time \(T\) of 12.8 ms. Then, each random speckle pattern is modulated by DMD-1 to 8 \(\times\) 8-resolution. We set four speckle patterns in each cycle to obtain four frames of the object. The compressive sampling ratio of MIPO-CSGI, F-CSGI, and S-CSGI is set up to 70.31%, which means there will be 180 differential bucket detector signals obtained in each scheme. We will  
Figure 4: The numerical simulation results of different periodic state-changed objects using F-CSGI, S-CSGI, and MIPO-CSGI, where _PSNR_ is presented together. program a 180 \(\times\) 256-resolution random matrix in MIPO-CSGI, and the sampling rate of the bucket detector in F-CSGI will be four times that of MIPO-CSGI and S-CSGI. Meanwhile, the total optical measurement time used in MIPO-CSGI and S-CSGI is four times that in F-CSGI. Figure 6 shows the results of two sets of experiments using MIPO-CSGI, F-CSGI, and S-CSGI. The experimental results-1 of MIPO-CSGI, F-CSGI, and S-CSGI are similar to the simulation results in Figure 2. Because the detection results are inevitably influenced by background and detector noise, even when the compression ratio is high, the reconstructed image quality of the experimental results in Figure 6 will be lower than the numerical simulation results in Figure 2. The bucket detector has a slow sampling rate and a long sampling time, which means that more environmental noise is introduced. Therefore, there is a certain attenuation in the imaging quality of MIPO-CSGI compared with F-CSGI in the experiment. It is known that DMD requires a certain amount of time to refresh and display different objects. During the experiment, we may obtain the intermediate states displayed by the DMD-2 switching between two frames, as shown in experimental results-2. As illustrated in Figure 6, although F-CSGI has a better reconstruction ability than MIPO-CSGI, it is hard to obtain information about the object when a slow bucket detector is used in S-CSGI. To obtain eight frames of the object, eight \(8\times 8\)-resolution speckle patterns with their inverse speckle patterns are generated in each cycle time \(T\). With a 70.31% compressive sampling ratio, a 360 \(\times\) 512-resolution random matrix will be recorded by computer-1 in MIPO-CSGI. All of these schemes will record 360 differential bucket detector signals. Meanwhile, the bucket detector's sampling rate in F-CSGI is set to be eight times that in MIPO-CSGI and S-CSGI.