# Design of Robust Sensing Matrix for UAV Images Encryption and Compression
## 4 Experiment
The enhancement SSIMs of value between the best algorithm \(CS_{LESM}\) and the second best one are 1.06% when the noise \(SNR=\) 20 dB, 0.44% when the noise \(SNR=\) 30 dB and 0.23% when no noise \(SNR=\infty\). * The reduction ratio of SRE between the best algorithm \(CS_{LESM}\) and the second best one is 10.43% when the noise \(SNR=\) 20 dB, 7.32% when the noise \(SNR=\) 30 dB and 5.61% when no noise \(SNR=\infty\). In addition, the more noise there is, the larger the recovery results of the SREs are. * Regarding the images with larger noise, the performance of algorithm \(CS_{HZ}\) is similar with algorithm \(CS_{LZY}\), but it is better than algorithm \(CS_{LLL}\). Hence, the ability of algorithm \(CS_{LLL}\) to resist large noise is weak. Figure 2: The original UAV images selected randomly from each eleven classifications of the “uav123” datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Bike**} & \multicolumn{8}{c}{**Bird**} \\ \hline  & \multicolumn{2}{c}{\(M\) = 20**} & \multicolumn{2}{c}{\(M\) = 24**} & \multicolumn{2}{c}{\(M\) = 20**} & \multicolumn{2}{c}{\(M\)  
**Remark 4**. :
* Without extra noise, observe the value of PSNRs and SSIMs in Table 4 and the visual feelings in Figures 3-5, where the sensing matrix algorithms \(CS_{LLL}\), \(CS_{HZ}\), and \(CS_{LESM}\) considering the SRE possess better recovery performance than the \(CS_{RAN}\), and \(CS_{LZY}\). Compared with algorithm \(CS_{LLL}\), the recovery accuracy of \(CS_{HZ}\) is worse than \(CS_{LLL}\). The proposed algorithm \(CS_{LESM}\) obtains the best recovery results for these three scenarios. All the results obtained from the experiments are consistent with those theoretical analyses. * With adding extra noise (\(SNR\) = 10 dB), observing the value of PSNRs and SSIMs in Table 4 and the visual feelings in Figures 6-8, the sensing matrix algorithms \(CS_{LZY}\), \(CS_{LLL}\), and \(CS_{HZ}\) possess a similar recovery performance. The proposed algorithm \(CS_{LESM}\) obtains the best recovery results for these three scenarios. * Compared with the original images in Figures 3a, 4a, 5a, 6a, 7a, 8a and other recovery results in Figures 3b-e, 4b-e, 5b-e, 6b-e, 7b-e, 8b-e, the details of recovery by adopting the proposed sensing matrix \(CS_{LESM}\) are the best. For instance, the corner of the building in \"autumn\", the colorful light lines in \"night view\", the logo of the shoes in \"friends\" are the clearest. Figure 3: Recovery results for the UAV image “autumn” taken by ourselves with no added noise. (**a**) is the original image, (**b**) is the \(CS_{RAN}\) algorithm, (**c**) is the \(CS_{LZY}\) algorithm, (**d**) is the \(CS_{LLL}\) algorithm, (**e**) is the \(CS_{HZ}\) algorithm, and (**f**) is the \(CS_{LESM}\) algorithm. The images of \"Night view\" contain much noise by themselves due to the bad lights. Hence, the recovery results are similar with or without the extra noise. This \"Night view\" with more noise indicates that the recovery accuracy can be improved by reducing sparse representation errors. * By using the proposed algorithm \(CS_{LESM}\) of the sensing matrix, the experiments on both UAV123 datasets and the images taken by ourselves reveal superior recovery accuracy. The larger the noise the image contains, the more obvious the improvement space of the recovery results is. Figure 4: Recovery results for the UAV image ”night view” taken by ourselves with no adding noise. (**a**) is the original image, (**b**) is the \(CS_{RAN}\) algorithm, (**c**) is the \(CS_{LZY}\) algorithm, (**d**) is the \(CS_{LLL}\) algorithm, (**e**) is the \(CS_{HZ}\) algorithm, and (**f**) is the \(CS_{LESM}\) algorithm. Figure 5: Recovery results for the UAV image “friends” taken by ourselves with no adding noise. (**a**) is the original image, (**b**) is the \(CS_{RAN}\) algorithm, (**c**) is the \(CS_{LZY}\) algorithm, (**d**) is the \(CS_{LLL}\) algorithm, (**e**) is the \(CS_{HZ}\) algorithm, and (**f**) is the \(CS_{LESM}\) algorithm.