## 4 Results  
The general data processing is applied as presented previously to measure the impact of the RH fluctuations on both samples of spruce of thickness 19 mm and 50 mm. For one sample, the amount of data is more than 500 images to be processed. The data acquired by DHSPI are presented in Figure 5 from image (a) to image (e) for time \(t_{17}\) and images from (f) to (j) after five minutes at the time \(t_{18}\). With Equation (5) is obtained image (k), the interferogram between time \(t_{17}\) and \(t_{18}\). Figure 4: Sample 51 (**left**), spruce dimensions of 100 \(\times\) 100 mm and a thickness of 19 mm. Sample 44 (**right**), spruce dimensions 100 \(\times\) 100 mm and a thickness of 50 mm. Between \(t_{17}\) and \(t_{18}\), there is a variation from 72% to 71%. A first pattern could be identified for these five minutes. Then, the impact of higher RH variations is followed during all the experimenting time. Then, the efficiency of the Matlab denoising median filter,'medfil2', is evaluated, as its different parameters, the padding and the window size. The three different padding available in the Matlab library were tested on the interferogram of sample 51 between the 85 th min and 90 th min, with the default window size of 3 by 3, the results are visible in Figure 3. Image (a) is the zeros padding, (b) is the symmetric padding and (c) is the indexed padding. Some of the main differences between the three paddings are represented by a circle with different colours, respectively. In Figure 6 images (a) and image (c) rounded in green some remaining dots represent noise that are results and the image. It can be deduced that less noise is seen in image (b), therefore, the symmetric padding is the best padding for our median filter. It appears with this first data treatment evaluation that the choice of symmetric padding seems to be the most efficient for these experimentations and these recording conditions. With this padding, one needs now to determine the best square window size allowing us to optimise the details determination and the most efficient contrast visualisation. The denoised image by the median filter,'medfiltz' by Matlab, is determined by the window size and has been tested from 2 \(\times\) 2 to 7 \(\times\) 7. The results of these tests are presented in  
Figure 5: Pre-processing result: interferogram between time t = 85 min and time t = 90 min of sample n\({}^{\circ}\)51. Data acquired by DHSPI from image (**a**–**e**) for time \(t_{17}\) and images from (**f**–**j**) after five minutes at the time \(t_{18}\). Image (**k**) is the interferogram between time \(t_{17}\) and \(t_{18}\) obtained from Equation (5). Figure 6: ‘medfiltz’ different with window default window size padding results on interferogram of sample 51 between 85 th min and 90 th min: (**a**) zeros padding, (**b**) symmetric padding and (c) indexed padding. The circles represent the main difference in the three images, the image (**b**) with the symmetric padding gives less noise. Figure 7 with symmetric padding on the interferogram of sample 51 between the beginning of the experiment and five minutes later for images (a) to (f) and on 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's beginning for images (g) to (l). The more the window has a larger size (\(7\times 7\)), the more the contrast is important. However, at the same time, an increase can be observed in the fading of details. Then, the best relevant compromise needs to be found for the aims and application of this work. From image (b), the grown rings can be observed, which is not possible in image (a). The window size of \(2\times 2\) does not denoise enough. From image (j), the details start to fade so the window size is too large from \(5\times 5\). The larger the window is, the more blurred areas appear.