3.3 | Comparison of U-Net architecture outcome with traditional noise removal algorithms
Our U-Net architecture is compared to the outcomes generated by the traditional algorithms like Wiener, Median, TV and SG filtering that were not reported in previous studies involving deep learning technologies and LED based photoacoustic images [54, 55]. The result of the subcutaneous tumor images of three different mice applying all those algorithms along with the low number of frame averaged inputs is shown in Fig. 6. It is to be noted that the varying amount of noise for each of the input images was intentionally chosen to evaluate noise invariancy of our U-Net algorithm with respect to the noise levels. The noise level for mouse 2 is the lowest among the three data sets shown and that of mouse 3 is highest (achieved by varying the gain range). The images for mice #1, #2, and #3 were acquired with 22-, 19-, and 25-dB gain, respectively. We did a quantitative comparison of all the different noise removal algorithms along with our U-Net and the box plots for PSNR, SNR and CNR are shown in Fig. 6 (h), 6 (p), & 6 (x), respectively. When we compared the performance of our model network with the traditional noise removal algorithms like Wiener, Median, TV, and SG filter, the U-Net images produced a better-enhanced version of outputs than the other ones which can visually be confirmed from Fig. 6 (b-g, j-o, and r-w). The different noise levels in Fig. 6 (b), (j), & (r) did not affect our network outcomes, which makes the U-Net framework a noise-level invariant model, while other established algorithms are not robust enough to deal with different kinds of noise profiles. In comparison with the other algorithms, U-Net emerges not only as a noise-robust process, but it maintains the original phantom structure that other methods are incapable of achieving. Wiener and SG filters blur the images along with SG showing an artifact similar to hazy motion artifact during acquisition. There is not much improvement in the signal and contrast concerning the background noise for the Median filter, a traditionally used noise filter. The TV-L1 filter reduced the noise homogeneously, however only the brightest parts of the images remained intact and a significant enhancement in background occurred in the entire image.