## 4 Results  
In this section, we present the results for the different images obtained. As we have mentioned, we used bit aggregates in the shape of linear stripes oriented vertically and horizontally to test the anamorphic effects. In addition, we changed the resolution in the image displayed: we used as information unit a group of pixels in the PA-LCoS. In Figure 7, we plot the images captured by the CCD camera when addressing the stripe data shape onto the PA-LCoS, respectively, for horizontal and vertical orientation for a bit size of 4 \(\times\) 4 pixels. These are the original images before the thresholding operation. The first thing to notice is the irregular illumination observed. This produces and image where a unique threshold level for the entire image does not exist. As we can vary the threshold level to make an image binarization, we can obtain some figures of merit to analyze the irregular illumination observed, and we can calculate the number of errors for all the threshold levels considered. The images depicted in Figure 7 are the ones captured by our CCD camera with a bit depth of 14 bits, so we have 16,384 possible gray levels. As long as we avoid saturating the CCD, we do not need to consider a full variation of all levels when we try to seek for the best threshold level. It is usually enough to consider a variation of 6000 levels, which is more than the typical extent of the overlapping zone (see Figure 6). Figure 8 presents the evolution in the number of errors counted as a function of the threshold level. The graph width gives us an idea about how sensitive is the information reconstruction to the threshold level. In fact, we observe how the number of errors decreases to a minimum and then rises again. The minimum level of light is not zero, since there always exists a thermal noise or a residual light intensity, thus it will be enough to start analyzing from a threshold level greater than 100, in our case we consider 200. The best threshold level would be the one that minimizes the number of errors. To be sure that we have selected the best threshold, we have to obtain a curve with an evolution, as the one reflected in either Figure 8a,b, which means that the number of error decreases until a minimum point and then the number of error rises again. That shape means that we have selected a range large enough to contain the threshold level that minimizes the number or errors. Figure 7: Original images captured for a 4 \(\times\) 4 pixel resolution horizontal and vertical orientations, previous to thresholding; (**a**): Captured image for horizontal stripes pattern (**b**): Captured image for vertical stripes pattern. To analyze the illumination influence, we now divide the image into four zones. Each of them can be analyzed in the same way described for the full image. In Figure 9, we show how we have defined the zones for the different orientations. Each zone is a different set of data to extract statistical results, as presented in the previous section. Figure 10 shows the different graphs for the zones defined in Figure 9 for vertical stripes. These zones have the same shape, but we can observe that they have slightly different best threshold levels for every zone. As long as the graph shape is preserved, the differences do not produce a significant increase in the number of errors. To maintain the possibility to compute statistical results, we do not reduce the size of the zones to look for illumination inhomogeneities. Reducing the size of the zones decreases the number of information bits available to calculate histograms. Nevertheless, as a qualitative evaluation tool, we have calculated the number of errors for every column (horizontal stripes) or row (vertical stripes). In this way, we see that there are columns (or rows) in which we identify all 0s and 1s correctly.