## 4 Results
The best windows for our application are \(3\times 3\) and \(4\times 4\), shown by images  
Figure 7: Denoising with Matlab function mediffl2 with symmetric padding. Interferogram of sample 51 between the beginning of the experiment and five minutes later denoised by a median filter with a window size of (a) \(2\times 2\), (b) \(3\times 3\), (c) \(4\times 4\), (d) \(5\times 5\), (e) \(6\times 6\) and (f) \(7\times 7\). The interferogram of sample 44 between the images acquired five minutes after the beginning of the experiment and the set of images taken after 10 min of the experiment with a window size of (g) \(2\times 2\), (h) \(3\times 3\), (i) \(4\times 4\), (j) \(5\times 5\), (k) \(6\times 6\) and (l) \(7\times 7\). (b), (c) and (h), (i) in Figure 7. The time of computing is slightly increasing with the increase of the size of the window. But this computing time stays under 0.15 s, a mean time of 20 images, which is quite negligible. For the chosen window size of 3 \(\times\) 3, the computing time is 0.04 s.  
Then, the second possible denoising algorithm SCA-SWT is tested. The method has been tested first on the raw interferogram Figure 8 image (a) and image (c) and on the interferogram denoised by the MatLab median filter with symmetric padding and the 3 \(\times\) 3 window Figure 8 images (b) and (d). The second denoising algorithm was not preceded by any previous denoising in the article of [PERSON] et al. [15]. Images (a) and (b) are tests on the interferogram of sample 51 between the beginning of the experiment and five minutes later whereas images (c) and (d) are tests on the interferogram of sample 44 between the beginning of the experiment and five minutes. It appears clearly that without previous denoising operated by a median filter with symmetric padding and a window size of 3 \(\times\) 3 one has lost too many details that will induce a lack of information. For this data treatment using the SCA-SWT, the work is performed with a wavelet db4 indicated in the article by [PERSON] et al. [15]. However, different analysis wavelets can be investigated and it is needed to optimise the different possible parameters. Figure 8: SCA-SWT denoising algorithm on interferogram of sample 51 between the beginning of the experiment and five minutes later (**a**) without any previous filter and (**b**) previously denoised by a median filter with symmetric padding and a window size of 3 \(\times\) 3, interferogram of sample 44 between the beginning of the experiment and five minutes later (**c**) without any previous filter and (**d**) previously denoised by a median filter with symmetric padding and a window size of 3 \(\times\) 3. To that end in Figure 9, the different kinds of wavelets were tested on an interferogram of sample 51 between 15 th min and 20 th min after the beginning of the experiment for images (a) to (d) and (i) to (l), the same processing was applied on interferogram of sample 44 between 5 th min and 10 th min after the beginning of the experiment for image (e) to (h) and (m) to (p). The results applied to the two mock-ups are shown with the shape of the wavelet. Before using the denoising process, the interferograms are denoised with the Matlab function'medfilt2' with parameters symmetric padding and a window size of 3 \(\times\) 3. The computing time is increasing with the increase of the peak number of the analysing wavelet. Two kinds of wavelets data treatment have been tested and compared Daubechies (db) and Symlets (sym) analysing wavelets to identify if our methodology is most relevant for the experiment which has been performed, the other parameters of the method are a five-level decomposition, an alpha equal to 6.25 and a Birge-Massart threshold. The SCA-SWT method takes less time with a db2 analysing wavelet than with a db4, but the computing time has a maximum of less than a second and a half for each image.