## 3 Results
### In Vivo Results Comparisons  
After setting the optimized window size for the wED algorithm, several other algorithms, aED [11,12], bED [12], and SV [7] were used to evaluate the performance of wED. In Figure 4, the same B-frame was processed with SV, aED, bED, and wED, which were shown in (A), (B), (C), and (D), respectively. Since only aED and wED could resolve blood flow signals, the background noise intensities of aED and wED results in Figure 4 were calculated by averaging the intensity within the regions that contained no OCTA signals (deep regions). The wED result had a noise intensity of 0.259, while the aED contributed to a noise intensity of 0.316.  
For further evaluation, en face results were generated by using maximum intensity projection (MIP) within the papillary dermis layers, which contained dense and small blood vessels when observing the pre-acquired data. In order to compare the algorithms quantitatively, a ground truth is desired. However, one is not available for in vivo studies. Therefore, we utilized a four-time registration result as the alternative ground truth. Four successive acquisitions at the same scanning location were processed by aED, then registered into one 3D dataset using the Elastix registration toolbox [45,46]. The four-time registration result would contain low noise and low motion artifacts, even though it had taken a long acquisition time. Hence, the four-time registration result was used as the reference image when calculating the PSNR values. In Figure 5, the same MIP processing was applied to all processed blood-flow datasets from (A) four-time registration, (B) SV,(C) aED, (D) bED, and (E) wED. For each processing method, three regions were enlarged to observe the algorithms' performance more clearly. Additionally, to present wED's consistent performance, two alternative datasets from different stages of wound healing were processed to generate the same comparisons with that in Figure 5, which are presented in Appendix A Figures A1 and A2.  
Figure 2: ROIs (red rectangles) with different depth ranges were applied to the same flattened raw data. The selected data were plotted in histograms, which were compared with fitted normal distributions. (**A**) All data from the slice, which were 4 repeated B-scans contributing a 340 \(\times\) 600 \(\times\) 4 matrix. (**B**) A large ROI with half of the depth range, 170 pixels, was selected. (**C**) A small ROI with a depth range of 40 pixels. (**D**) An extremely small depth range of 5 pixels. ROI: region of interest.  
Figure 4: B-scan comparison. The same B-scan frame with 4 repeating times was processed by (**A**) SV, (**B**) aED, (**C**) bED, and (**D**) wED, respectively. Regions that have differences in OCTA reconstruction were pointed out with red arrows. SV: Speckle Variance; aED: A-scan eigen-decomposition; bED: B-scan eigen-decomposition; wED: windowed eigen-decomposition.  
Figure 3: The average normal-distribution correlation coefficients with different window sizes.  
Figure 5: The comparisons of en face results from maximum intensity projection using different algorithms. (**A**) Registration from 4-time acquisitions; (**B**) SV; (**C**) aED; (**D**) bED; (**E**) wED. (All scale bars represent 500 \(\upmu\)m); SV: Speckle Variance; aED: A-scan eigen-decomposition; bED: B-scan eigen-decomposition; wED: windowed eigen-decomposition.