## 4 Results
For 564 images for processing of mock-up n\({}^{\circ}\)51 data, the processing takes from 8 min and 16 s for sym2 to 11 min and 43 s for sym8, and for 582 images for processing of sample 44 data, it takes 8 min and 42 s for sym2 to 11 min and 25 s for db8. The different images in Figure 9 do not seem to have that much of a difference. There is some information lost when the analysing wavelet is db4 or higher and sym4 or higher, this observation can be explained by the wavelet form. Indeed, the db2 and sym2 wavelets are sharper, which corresponds to the sharp edges found in the interferogram. Therefore, the db2 and sym2 analysing wavelet are possible for the expected application. The border extension of the image during this method can bring some problems that are rounded in Figure 9 image (a). This image is the denoised interferogram of the beginning of the experiment and five minutes later sample 51, the image has a discontinuity on its border that can induce false displacement on the sides of the images. No discontinuities are visible for the other sample images. Therefore, the algorithm drawbacks depend on the processed interferogram and cannot be predicted. After the last step, the unwrapping method is tested based on the CPULSI algorithm including the evaluation of the limit suitable iterations and minimising the error. The value \(\text{ite}_{\text{Max}}\)300e0.001 has a suitable limit at 300 iterations and an error to reach 0.001. The phase unwrapped by FORTH software is also plotted to compare our results. The CPULSI algorithm did not unwrap a denoised image in the article of [PERSON] et al. (2019), this method is tested. The Phase Unwrapping based on Least-Squares (PULSI) results available by the Matlab function are also presented, this algorithm presents the advantage of not using calibration and is suitable for denoised images. The CPULSI algorithm needs a phase-known point that is chosen as the point in the raw or denoised interferogram that has the phase nearer to zero taking the minimum in absolute value in the entire image. The denoised images are obtained with a median filter with symmetric padding and a window size of 3 \(\times\) 3, followed by the SCA-SWT algorithm with a db2 analysing wavelet, in Figure 10 interferograms between the beginning of the experiment and five minutes later of experiment on sample n\({}^{\circ}\)51 are used whereas in Figure 11 interferogram between five minutes after the beginning of the experiment and five minutes later on sample n\({}^{\circ}\)44 are tested. Figure 9: Denoising with Matlab function ‘medfilt2’ with symmetric padding and window size 3 \(\times\) 3 followed by the algorithm SCA-SWT with a five-level decomposition, a default sigma found through ‘wnoises’, an alpha equal to 6.25 and a Birgé-Massart threshold. Interferogram of sample 51 between 15 th min and 20 th min after the beginning of the experiment with (**a**) a db2 analysing wavelet, (**b**) a db4 analysing wavelet, (**c**) a db6 analysing wavelet and (**d**) a db8 analysing wavelet. Interferogram of sample 44 between 5 th min and 10 th min after the beginning of the experiment with (**e**) a db2 analysing wavelet, (**f**) a db4 analysing wavelet, (**g**) a db6 analysing wavelet and (**h**) a db8 analysing wavelet. Interferogram of sample 51 between 15 th min and 20 th min after the beginning of the experiment with (**i**) a sym2 analysing wavelet, (**j**) a sym4 analysing wavelet, (**k**) a sym6 analysing wavelet and (**l**) a sym8 analysing wavelet. Interferogram of sample 44 between 5 min and 10 min after the beginning of the experiment, (**m**) a sym2 analysing wavelet, (**n**) a sym4 analysing wavelet, (**o**) a sym6 analysing wavelet, (**p**) a sym8 analysing wavelet.